BIOMARKERS AND METHODS FOR PREDICTING PRETERM BIRTH

Abstract

The disclosure provides biomarker panels, methods and kits for determining the probability for preterm birth in a pregnant female. The present disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of developing in the future or presently suffering from preterm birth relative to matched controls. The present disclosure is further based, in part, on the unexpected discovery that panels combining one or more of these proteins and peptides can be utilized in methods of determining the probability for preterm birth in a pregnant female with relatively high sensitivity and specificity. These proteins and peptides disclosed herein serve as biomarkers for classifying test samples, predicting a probability of preterm birth, monitoring of progress of preterm birth in a pregnant female, either individually or in a panel of biomarkers.

Claims

1. A panel of isolated biomarkers comprising N of the biomarkers listed in Tables 1 through 63.

2. The panel of claim 1, wherein N is a number selected from the group consisting of 2 to 24.

3-6. (canceled)

7. A method of determining probability for preterm birth in a pregnant female, the method comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from said pregnant female, and analyzing said measurable feature to determine the probability for preterm birth in said pregnant female.

8. The method of claim 7, wherein said measurable feature comprises fragments or derivatives of each of said N biomarkers selected from the biomarkers listed in Tables 1 through 63.

9. The method of claim 7, wherein said detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

10. (canceled)

11. The method of claim 7, further comprising an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 1 through 63.

12. The method of claim 7, further comprising an initial step of providing a biological sample from the pregnant female.

13-14. (canceled)

15. The method of claim 7, wherein N is a number selected from the group consisting of 2 to 24.

16-37. (canceled)

38. A method of predicting GAB, the method comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from a pregnant female, and analyzing said measurable feature to predict GAB.

39. The method of claim 38, wherein said measurable feature comprises fragments or derivatives of each of said N biomarkers selected from the biomarkers listed in Tables 1 through 63.

40. The method of claim 38, wherein said detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

41-42. (canceled)

43. The method of claim 38, further comprising an initial step of providing a biological sample from the pregnant female.

44-79. (canceled)

80. A method of detecting and/or quantifying one or more biomarkers selected from biomarkers listed in Tables 1 through 63 in a biological sample from a pregnant female, said method comprising: a. obtaining said biological sample from a pregnant female; and b. detecting whether said one or more biomarkers are present in the biological sample comprising subjecting the sample to mass spectrometry, a capture agent or a combination thereof.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0035] FIG. 1. Scatterplot of actual gestational age at birth versus predicted gestational age from random forest regression model.

[0036] FIG. 2. Distribution of predicted gestational age from random forest regression model versus actual gestational age at birth (GAB), where actual GAB is given in categories of (i) less than 37 weeks, (ii) 37 to 39 weeks, and (iii) 40 weeks or greater (peaks left to right, respectively).

DETAILED DESCRIPTION

[0037] The present disclosure is based, in part, on the discovery that certain proteins and peptides in biological samples obtained from a pregnant female are differentially expressed in pregnant females that have an increased risk of preterm birth relative to controls. The present disclosure is further based, in part, on the unexpected discovery that panels combining one or more of these proteins and peptides can be utilized in methods of determining the probability for preterm birth in a pregnant female with high sensitivity and specificity. These proteins and peptides disclosed herein serve as biomarkers for classifying test samples, predicting probability of preterm birth, predicting probability of term birth, predicting gestational age at birth (GAB), predicting time to birth and/or monitoring of progress of preventative therapy in a pregnant female, either individually or in a panel of biomarkers.

[0038] The disclosure provides biomarker panels, methods and kits for determining the probability for preterm birth in a pregnant female. One major advantage of the present disclosure is that risk of developing preterm birth can be assessed early during pregnancy so that appropriate monitoring and clinical management to prevent preterm delivery can be initiated in a timely fashion. The present invention is of particular benefit to females lacking any risk factors for preterm birth and who would not otherwise be identified and treated.

[0039] By way of example, the present disclosure includes methods for generating a result useful in determining probability for preterm birth in a pregnant female by obtaining a dataset associated with a sample, where the dataset at least includes quantitative data about biomarkers and panels of biomarkers that have been identified as predictive of preterm birth, and inputting the dataset into an analytic process that uses the dataset to generate a result useful in determining probability for preterm birth in a pregnant female. As described further below, this quantitative data can include amino acids, peptides, polypeptides, proteins, nucleotides, nucleic acids, nucleosides, sugars, fatty acids, steroids, metabolites, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof.

[0040] In addition to the specific biomarkers identified in this disclosure, for example, by accession number in a public database, sequence, or reference, the invention also contemplates use of biomarker variants that are at least 90% or at least 95% or at least 97% identical to the exemplified sequences and that are now known or later discovered and that have utility for the methods of the invention. These variants may represent polymorphisms, splice variants, mutations, and the like. In this regard, the instant specification discloses multiple art-known proteins in the context of the invention and provides exemplary accession numbers associated with one or more public databases as well as exemplary references to published journal articles relating to these art-known proteins. However, those skilled in the art appreciate that additional accession numbers and journal articles can easily be identified that can provide additional characteristics of the disclosed biomarkers and that the exemplified references are in no way limiting with regard to the disclosed biomarkers. As described herein, various techniques and reagents find use in the methods of the present invention. Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine. In some embodiments, the biological sample is selected from the group consisting of whole blood, plasma, and serum. In a particular embodiment, the biological sample is serum. As described herein, biomarkers can be detected through a variety of assays and techniques known in the art. As further described herein, such assays include, without limitation, mass spectrometry (MS)-based assays, antibody-based assays as well as assays that combine aspects of the two.

[0041] Protein biomarkers associated with the probability for preterm birth in a pregnant female include, but are not limited to, one or more of the isolated biomarkers listed in Tables 1 through 63. In addition to the specific biomarkers, the disclosure further includes biomarker variants that are about 90%, about 95%, or about 97% identical to the exemplified sequences. Variants, as used herein, include polymorphisms, splice variants, mutations, and the like.

[0042] Additional markers can be selected from one or more risk indicia, including but not limited to, maternal characteristics, medical history, past pregnancy history, and obstetrical history. Such additional markers can include, for example, previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, short cervical length measurements, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight, low or high body mass index, diabetes, hypertension, urogenital infections (i.e. urinary tract infection), asthma, anxiety and depression, asthma, hypertension, hypothyroidism. Demographic risk indicia for preterm birth can include, for example, maternal age, race/ethnicity, single marital status, low socioeconomic status, maternal age, employment-related physical activity, occupational exposures and environment exposures and stress. Further risk indicia can include, inadequate prenatal care, cigarette smoking, use of marijuana and other illicit drugs, cocaine use, alcohol consumption, caffeine intake, maternal weight gain, dietary intake, sexual activity during late pregnancy and leisure-time physical activities. (Preterm Birth: Causes, Consequences, and Prevention, Institute of Medicine (US) Committee on Understanding Premature Birth and Assuring Healthy Outcomes; Behrman RE, Butler AS, editors. Washington (DC): National Academies Press (US); 2007). Additional risk indicia useful for as markers can be identified using learning algorithms known in the art, such as linear discriminant analysis, support vector machine classification, recursive feature elimination, prediction analysis of microarray, logistic regression, CART, FlexTree, LART, random forest, MART, and/or survival analysis regression, which are known to those of skill in the art and are further described herein.

[0043] Provided herein are panels of isolated biomarkers comprising N of the biomarkers selected from the group listed in Tables 1 through 63. In the disclosed panels of biomarkers N can be a number selected from the group consisting of 2 to 24. In the disclosed methods, the number of biomarkers that are detected and whose levels are determined, can be 1, or more than 1, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or more. In certain embodiments, the number of biomarkers that are detected, and whose levels are determined, can be 1, or more than 1, such as 2, 3, 4, 5, 6, 7, 8, 9, 10, or more. The methods of this disclosure are useful for determining the probability for preterm birth in a pregnant female.

[0044] While certain of the biomarkers listed in Tables 1 through 63 are useful alone for determining the probability for preterm birth in a pregnant female, methods are also described herein for the grouping of multiple subsets of the biomarkers that are each useful as a panel of three or more biomarkers. In some embodiments, the invention provides panels comprising N biomarkers, wherein N is at least three biomarkers. In other embodiments, N is selected to be any number from 3-23 biomarkers.

[0045] In yet other embodiments, N is selected to be any number from 2-5, 2-10, 2-15, 2-20, or 2-23. In other embodiments, N is selected to be any number from 3-5, 3-10, 3-15, 3-20, or 3-23. In other embodiments, N is selected to be any number from 4-5, 4-10, 4-15, 4-20, or 4-23. In other embodiments, N is selected to be any number from 5-10, 5-15, 5-20, or 5-23. In other embodiments, N is selected to be any number from 6-10, 6-15, 6-20, or 6-23. In other embodiments, N is selected to be any number from 7-10, 7-15, 7-20, or 7-23. In other embodiments, N is selected to be any number from 8-10, 8-15, 8-20, or 8-23. In other embodiments, N is selected to be any number from 9-10, 9-15, 9-20, or 9-23. In other embodiments, N is selected to be any number from 10-15, 10-20, or 10-23. It will be appreciated that N can be selected to encompass similar, but higher order, ranges.

[0046] In certain embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, or five isolated biomarkers comprising an amino acid sequence selected from AFTECCVVASQLR, ELLESYIDGR, ITLPDFTGDLR, TDAPDLPEENQAR and SFRPFVPR. In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, three or more, four or more, or five isolated biomarkers comprising an amino acid sequence selected from FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

[0047] In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, or three of the isolated biomarkers consisting of an amino acid sequence selected from AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, or three of the isolated biomarkers consisting of an amino acid sequence selected from FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

[0048] In some embodiments, the panel of isolated biomarkers comprises one or more, two or more, or three of the isolated biomarkers consisting of an amino acid sequence selected from the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

[0049] In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from lipopolysaccharide-binding protein (LBP), Schumann et al., Science 249 (4975), 1429-1431 (1990) (UniProtKB/Swiss-Prot: P18428.3); prothrombin (THRB), Walz et al., Proc. Natl. Acad. Sci. U.S.A. 74 (5), 1969-1972(1977) (NCBI Reference Sequence: NP_000497.1); complement component C5 (C5 or CO5) Haviland, J. Immunol. 146 (1), 362-368 (1991) (GenBank: AAA51925.1); plasminogen (PLMN) Petersen et al., J. Biol. Chem. 265 (11), 6104-6111(1990) (NCBI Reference Sequences: NP_000292.1 NP_001161810.1); and complement component C8 gamma chain (C8G or CO8G), Haefliger et al., Mol. Immunol. 28 (1-2), 123-131 (1991) (NCBI Reference Sequence: NP_000597.2).

[0050] In some embodiments, the panel of isolated biomarkers comprises one or more peptides comprising a fragment from cell adhesion molecule with homology to complement component 1, q subcomponent, B chain (C1QB), Reid, Biochem. J. 179 (2), 367-371 (1979) (NCBI Reference Sequence: NP_000482.3); fibrinogen beta chain (FIBB or FIB); Watt et al., Biochemistry 18 (1), 68-76 (1979) (NCBI Reference Sequences: NP_001171670.1 and NP_005132.2); C-reactive protein (CRP), Oliveira et al., J. Biol. Chem. 254 (2), 489-502 (1979) (NCBI Reference Sequence: NP_000558.2); inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4) Kim et al., Mol. Biosyst. 7 (5), 1430-1440 (2011) (NCBI Reference Sequences: NP_001159921.1 and NP_002209.2); chorionic somatomammotropin hormone (CSH) Selby et al., J. Biol. Chem. 259 (21), 13131-13138 (1984) (NCBI Reference Sequence: NP_001308.1); and angiotensinogen (ANG or ANGT) Underwood et al., Metabolism 60(8):1150-7 (2011) (NCBI Reference Sequence: NP_000020.1).

[0051] In additional embodiments, the invention provides a panel of isolated biomarkers comprising N of the biomarkers listed in Tables 1 through 63. In some embodiments, N is a number selected from the group consisting of 2 to 24. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, ITLPDFTGDLR, TDAPDLPEENQAR and SFRPFVPR. In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

[0052] In additional embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

[0053] In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G). In another embodiment, the invention provides a biomarker panel comprising at least three isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

[0054] In further embodiments, the biomarker panel comprises at least two of the isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

[0055] In some embodiments, the invention provides a biomarker panel comprising lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT). In some embodiments, the invention provides a biomarker panel comprising Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

[0056] In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT) and the biomarkers set forth in Tables 51 and 53.

[0057] In another aspect, the invention provides a biomarker panel comprising at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

[0058] It must be noted that, as used in this specification and the appended claims, the singular forms “a”, “an” and “the” include plural referents unless the content clearly dictates otherwise. Thus, for example, reference to “a biomarker” includes a mixture of two or more biomarkers, and the like.

[0059] The term “about,” particularly in reference to a given quantity, is meant to encompass deviations of plus or minus five percent.

[0060] As used in this application, including the appended claims, the singular forms “a,” “an,” and “the” include plural references, unless the content clearly dictates otherwise, and are used interchangeably with “at least one” and “one or more.”

[0061] As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “contains,” “containing,” and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, product-by-process, or composition of matter that comprises, includes, or contains an element or list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, product-by-process, or composition of matter.

[0062] As used herein, the term “panel” refers to a composition, such as an array or a collection, comprising one or more biomarkers. The term can also refer to a profile or index of expression patterns of one or more biomarkers described herein. The number of biomarkers useful for a biomarker panel is based on the sensitivity and specificity value for the particular combination of biomarker values.

[0063] As used herein, and unless otherwise specified, the terms “isolated” and “purified” generally describes a composition of matter that has been removed from its native environment (e.g., the natural environment if it is naturally occurring), and thus is altered by the hand of man from its natural state. An isolated protein or nucleic acid is distinct from the way it exists in nature.

[0064] The term “biomarker” refers to a biological molecule, or a fragment of a biological molecule, the change and/or the detection of which can be correlated with a particular physical condition or state. The terms “marker” and “biomarker” are used interchangeably throughout the disclosure. For example, the biomarkers of the present invention are correlated with an increased likelihood of preterm birth. Such biomarkers include, but are not limited to, biological molecules comprising nucleotides, nucleic acids, nucleosides, amino acids, sugars, fatty acids, steroids, metabolites, peptides, polypeptides, proteins, carbohydrates, lipids, hormones, antibodies, regions of interest that serve as surrogates for biological macromolecules and combinations thereof (e.g., glycoproteins, ribonucleoproteins, lipoproteins). The term also encompasses portions or fragments of a biological molecule, for example, peptide fragment of a protein or polypeptide that comprises at least 5 consecutive amino acid residues, at least 6 consecutive amino acid residues, at least 7 consecutive amino acid residues, at least 8 consecutive amino acid residues, at least 9 consecutive amino acid residues, at least 10 consecutive amino acid residues, at least 11 consecutive amino acid residues, at least 12 consecutive amino acid residues, at least 13 consecutive amino acid residues, at least 14 consecutive amino acid residues, at least 15 consecutive amino acid residues, at least 5 consecutive amino acid residues, at least 16 consecutive amino acid residues, at least 17 consecutive amino acid residues, at least 18 consecutive amino acid residues, at least 19 consecutive amino acid residues, at least 20 consecutive amino acid residues, at least 21 consecutive amino acid residues, at least 22 consecutive amino acid residues, at least 23 consecutive amino acid residues, at least 24 consecutive amino acid residues, at least 25 consecutive amino acid residues, or more consecutive amino acid residues.

[0065] The invention also provides a method of determining probability for preterm birth in a pregnant female, the method comprising detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from the pregnant female, and analyzing the measurable feature to determine the probability for preterm birth in the pregnant female. As disclosed herein, a measurable feature comprises fragments or derivatives of each of said N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments of the disclosed methods detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

[0066] The invention further provides a method of predicting GAB, the method encompassing detecting a measurable feature of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in a biological sample obtained from a pregnant female, and analyzing the measurable feature to predict GAB.

[0067] The invention also provides a method of predicting GAB, the method comprising: (a) quantifying in a biological sample obtained from the pregnant female an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63; (b) multiplying or thresholding the amount by a predetermined coefficient, (c) determining the predicted GAB birth in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the predicted GAB.

[0068] The invention further provides a method of predicting time to birth in a pregnant female, the method comprising: (a) obtaining a biological sample from the pregnant female; (b) quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63 in the biological sample; (c) multiplying or thresholding the amount by a predetermined coefficient, (d) determining predicted GAB in the pregnant female comprising adding the individual products to obtain a total risk score that corresponds to the predicted GAB; and (e) subtracting the estimated gestational age (GA) at time biological sample was obtained from the predicted GAB to predict time to birth in said pregnant female. For methods directed to predicting time to birth, it is understood that “birth” means birth following spontaneous onset of labor, with or without rupture of membranes.

[0069] Although described and exemplified with reference to methods of determining probability for preterm birth in a pregnant female, the present disclosure is similarly applicable to the methods of predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female. It will be apparent to one skilled in the art that each of the aforementioned methods has specific and substantial utilities and benefits with regard maternal-fetal health considerations.

[0070] In some embodiments, the method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of N biomarkers, wherein N is selected from the group consisting of 2 to 24. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. In further embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

[0071] In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 50 and the biomarkers set forth in Table 52.

[0072] In additional embodiments, the method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

[0073] In additional embodiments, the method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

[0074] In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), complement component C8 gamma chain (C8G or CO8G), complement component 1, q subcomponent, B chain (C1QB), fibrinogen beta chain (FIBB or FIB), C-reactive protein (CRP), inter-alpha-trypsin inhibitor heavy chain H4 (ITIH4), chorionic somatomammotropin hormone (CSH), and angiotensinogen (ANG or ANGT).

[0075] In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

[0076] In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

[0077] In further embodiments, the disclosed method of determining probability for preterm birth in a pregnant female and related methods disclosed herein comprise detecting a measurable feature of each of at least two isolated biomarkers selected from the group consisting of the biomarkers set forth in Table 51 and the biomarkers set forth in Table 53.

[0078] In additional embodiments, the methods of determining probability for preterm birth in a pregnant female further encompass detecting a measurable feature for one or more risk indicia associated with preterm birth. In additional embodiments the risk indicia are selected form the group consisting of previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight, low or high body mass index, diabetes, hypertension, and urogenital infections.

[0079] A “measurable feature” is any property, characteristic or aspect that can be determined and correlated with the probability for preterm birth in a subject. The term further encompasses any property, characteristic or aspect that can be determined and correlated in connection with a prediction of GAB, a prediction of term birth, or a prediction of time to birth in a pregnant female. For a biomarker, such a measurable feature can include, for example, the presence, absence, or concentration of the biomarker, or a fragment thereof, in the biological sample, an altered structure, such as, for example, the presence or amount of a post-translational modification, such as oxidation at one or more positions on the amino acid sequence of the biomarker or, for example, the presence of an altered conformation in comparison to the conformation of the biomarker in normal control subjects, and/or the presence, amount, or altered structure of the biomarker as a part of a profile of more than one biomarker. In addition to biomarkers, measurable features can further include risk indicia including, for example, maternal characteristics, age, race, ethnicity, medical history, past pregnancy history, obstetrical history. For a risk indicium, a measurable feature can include, for example, previous low birth weight or preterm delivery, multiple 2nd trimester spontaneous abortions, prior first trimester induced abortion, familial and intergenerational factors, history of infertility, nulliparity, placental abnormalities, cervical and uterine anomalies, short cervical length measurements, gestational bleeding, intrauterine growth restriction, in utero diethylstilbestrol exposure, multiple gestations, infant sex, short stature, low prepregnancy weight/low body mass index, diabetes, hypertension, urogenital infections, hypothyroidism, asthma, low educational attainment, cigarette smoking, drug use and alcohol consumption.

[0080] In some embodiments of the disclosed methods of determining probability for preterm birth in a pregnant female, the probability for preterm birth in the pregnant female is calculated based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63. In some embodiments, the disclosed methods for determining the probability of preterm birth encompass detecting and/or quantifying one or more biomarkers using mass spectrometry, a capture agent or a combination thereof.

[0081] In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass an initial step of providing a biomarker panel comprising N of the biomarkers listed in Tables 1 through 63. In additional embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass an initial step of providing a biological sample from the pregnant female.

[0082] In some embodiments, the disclosed methods of determining probability for preterm birth in a pregnant female encompass communicating the probability to a health care provider. The disclosed of predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female similarly encompass communicating the probability to a health care provider. As stated above, although described and exemplified with reference to determining probability for preterm birth in a pregnant female, all embodiments described throughout this disclosure are similarly applicable to the methods of predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female. Specifically, the biomarkers and panels recited throughout this application with express reference to methods for preterm birth can also be used in methods for predicting GAB, the methods for predicting term birth, methods for determining the probability of term birth in a pregnant female as well methods of predicting time to birth in a pregnant female. It will be apparent to one skilled in the art that each of the aforementioned methods have specific and substantial utilities and benefits with regard maternal-fetal health considerations.

[0083] In additional embodiments, the communication informs a subsequent treatment decision for the pregnant female. In some embodiments, the method of determining probability for preterm birth in a pregnant female encompasses the additional feature of expressing the probability as a risk score.

[0084] As used herein, the term “risk score” refers to a score that can be assigned based on comparing the amount of one or more biomarkers in a biological sample obtained from a pregnant female to a standard or reference score that represents an average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females. Because the level of a biomarker may not be static throughout pregnancy, a standard or reference score has to have been obtained for the gestational time point that corresponds to that of the pregnant female at the time the sample was taken. The standard or reference score can be predetermined and built into a predictor model such that the comparison is indirect rather than actually performed every time the probability is determined for a subject. A risk score can be a standard (e.g., a number) or a threshold (e.g., a line on a graph). The value of the risk score correlates to the deviation, upwards or downwards, from the average amount of the one or more biomarkers calculated from biological samples obtained from a random pool of pregnant females. In certain embodiments, if a risk score is greater than a standard or reference risk score, the pregnant female can have an increased likelihood of preterm birth. In some embodiments, the magnitude of a pregnant female's risk score, or the amount by which it exceeds a reference risk score, can be indicative of or correlated to that pregnant female's level of risk.

[0085] In the context of the present invention, the term “biological sample,” encompasses any sample that is taken from pregnant female and contains one or more of the biomarkers listed in Tables 1 through 63. Suitable samples in the context of the present invention include, for example, blood, plasma, serum, amniotic fluid, vaginal secretions, saliva, and urine. In some embodiments, the biological sample is selected from the group consisting of whole blood, plasma, and serum. In a particular embodiment, the biological sample is serum. As will be appreciated by those skilled in the art, a biological sample can include any fraction or component of blood, without limitation, T cells, monocytes, neutrophils, erythrocytes, platelets and microvesicles such as exosomes and exosome-like vesicles. In a particular embodiment, the biological sample is serum.

[0086] Preterm birth refers to delivery or birth at a gestational age less than 37 completed weeks. Other commonly used subcategories of preterm birth have been established and delineate moderately preterm (birth at 33 to 36 weeks of gestation), very preterm (birth at <33 weeks of gestation), and extremely preterm (birth at ≤28 weeks of gestation). With regard to the methods disclosed herein, those skilled in the art understand that the cut-offs that delineate preterm birth and term birth as well as the cut-offs that delineate subcategories of preterm birth can be adjusted in practicing the methods disclosed herein, for example, to maximize a particular health benefit. It is further understood that such adjustments are well within the skill set of individuals considered skilled in the art and encompassed within the scope of the inventions disclosed herein. Gestational age is a proxy for the extent of fetal development and the fetus's readiness for birth. Gestational age has typically been defined as the length of time from the date of the last normal menses to the date of birth. However, obstetric measures and ultrasound estimates also can aid in estimating gestational age. Preterm births have generally been classified into two separate subgroups. One, spontaneous preterm births are those occurring subsequent to spontaneous onset of preterm labor or preterm premature rupture of membranes regardless of subsequent labor augmentation or cesarean delivery. Two, indicated preterm births are those occurring following induction or cesarean section for one or more conditions that the woman's caregiver determines to threaten the health or life of the mother and/or fetus. In some embodiments, the methods disclosed herein are directed to determining the probability for spontaneous preterm birth. In additional embodiments, the methods disclosed herein are directed to predicting gestational birth.

[0087] As used herein, the term “estimated gestational age” or “estimated GA” refers to the GA determined based on the date of the last normal menses and additional obstetric measures, ultrasound estimates or other clinical parameters including, without limitation, those described in the preceding paragraph. In contrast the term “predicted gestational age at birth” or “predicted GAB” refers to the GAB determined based on the methods of the invention as disclosed herein. As used herein, “term birth” refers to birth at a gestational age equal or more than 37 completed weeks.

[0088] In some embodiments, the pregnant female is between 17 and 28 weeks of gestation at the time the biological sample is collected. In other embodiments, the pregnant female is between 16 and 29 weeks, between 17 and 28 weeks, between 18 and 27 weeks, between 19 and 26 weeks, between 20 and 25 weeks, between 21 and 24 weeks, or between 22 and 23 weeks of gestation at the time the biological sample is collected. In further embodiments, the pregnant female is between about 17 and 22 weeks, between about 16 and 22 weeks between about 22 and 25 weeks, between about 13 and 25 weeks, between about 26 and 28, or between about 26 and 29 weeks of gestation at the time the biological sample is collected. Accordingly, the gestational age of a pregnant female at the time the biological sample is collected can be 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29 or 30 weeks.

[0089] In some embodiments of the claimed methods the measurable feature comprises fragments or derivatives of each of the N biomarkers selected from the biomarkers listed in Tables 1 through 63. In additional embodiments of the claimed methods, detecting a measurable feature comprises quantifying an amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63, combinations or portions and/or derivatives thereof in a biological sample obtained from said pregnant female.

[0090] The term “amount” or “level” as used herein refers to a quantity of a biomarker that is detectable or measurable in a biological sample and/or control. The quantity of a biomarker can be, for example, a quantity of polypeptide, the quantity of nucleic acid, or the quantity of a fragment or surrogate. The term can alternatively include combinations thereof. The term “amount” or “level” of a biomarker is a measurable feature of that biomarker.

[0091] In some embodiments, calculating the probability for preterm birth in a pregnant female is based on the quantified amount of each of N biomarkers selected from the biomarkers listed in Tables 1 through 63. Any existing, available or conventional separation, detection and quantification methods can be used herein to measure the presence or absence (e.g., readout being present vs. absent; or detectable amount vs. undetectable amount) and/or quantity (e.g., readout being an absolute or relative quantity, such as, for example, absolute or relative concentration) of biomarkers, peptides, polypeptides, proteins and/or fragments thereof and optionally of the one or more other biomarkers or fragments thereof in samples. In some embodiments, detection and/or quantification of one or more biomarkers comprises an assay that utilizes a capture agent. In further embodiments, the capture agent is an antibody, antibody fragment, nucleic acid-based protein binding reagent, small molecule or variant thereof. In additional embodiments, the assay is an enzyme immunoassay (EIA), enzyme-linked immunosorbent assay (ELISA), and radioimmunoassay (MA). In some embodiments, detection and/or quantification of one or more biomarkers further comprises mass spectrometry (MS). In yet further embodiments, the mass spectrometry is co-immunoprecipitation-mass spectrometry (co-IP MS), where coimmunoprecipitation, a technique suitable for the isolation of whole protein complexes is followed by mass spectrometric analysis.

[0092] As used herein, the term “mass spectrometer” refers to a device able to volatilize/ionize analytes to form gas-phase ions and determine their absolute or relative molecular masses. Suitable methods of volatilization/ionization are matrix-assisted laser desorption ionization (MALDI), electrospray, laser/light, thermal, electrical, atomized/sprayed and the like, or combinations thereof. Suitable forms of mass spectrometry include, but are not limited to, ion trap instruments, quadrupole instruments, electrostatic and magnetic sector instruments, time of flight instruments, time of flight tandem mass spectrometer (TOF MS/MS), Fourier-transform mass spectrometers, Orbitraps and hybrid instruments composed of various combinations of these types of mass analyzers. These instruments can, in turn, be interfaced with a variety of other instruments that fractionate the samples (for example, liquid chromatography or solid-phase adsorption techniques based on chemical, or biological properties) and that ionize the samples for introduction into the mass spectrometer, including matrix-assisted laser desorption (MALDI), electrospray, or nanospray ionization (ESI) or combinations thereof.

[0093] Generally, any mass spectrometric (MS) technique that can provide precise information on the mass of peptides, and preferably also on fragmentation and/or (partial) amino acid sequence of selected peptides (e.g., in tandem mass spectrometry, MS/MS; or in post source decay, TOF MS), can be used in the methods disclosed herein. Suitable peptide MS and MS/MS techniques and systems are well-known per se (see, e.g., Methods in Molecular Biology, vol. 146: “Mass Spectrometry of Proteins and Peptides”, by Chapman, ed., Humana Press 2000; Biemann 1990. Methods Enzymol 193: 455-79; or Methods in Enzymology, vol. 402: “Biological Mass Spectrometry”, by Burlingame, ed., Academic Press 2005) and can be used in practicing the methods disclosed herein. Accordingly, in some embodiments, the disclosed methods comprise performing quantitative MS to measure one or more biomarkers. Such quantitative methods can be performed in an automated (Villanueva, et al., Nature Protocols (2006) 1(2):880-891) or semi-automated format. In particular embodiments, MS can be operably linked to a liquid chromatography device (LC-MS/MS or LC-MS) or gas chromatography device (GC-MS or GC-MS/MS). Other methods useful in this context include isotope-coded affinity tag (ICAT), tandem mass tags (TMT), or stable isotope labeling by amino acids in cell culture (SILAC), followed by chromatography and MS/MS.

[0094] As used herein, the terms “multiple reaction monitoring (MRM)” or “selected reaction monitoring (SRM)” refer to an MS-based quantification method that is particularly useful for quantifying analytes that are in low abundance. In an SRM experiment, a predefined precursor ion and one or more of its fragments are selected by the two mass filters of a triple quadrupole instrument and monitored over time for precise quantification. Multiple SRM precursor and fragment ion pairs can be measured within the same experiment on the chromatographic time scale by rapidly toggling between the different precursor/fragment pairs to perform an MRM experiment. A series of transitions (precursor/fragment ion pairs) in combination with the retention time of the targeted analyte (e.g., peptide or small molecule such as chemical entity, steroid, hormone) can constitute a definitive assay. A large number of analytes can be quantified during a single LC-MS experiment. The term “scheduled,” or “dynamic” in reference to MRM or SRM, refers to a variation of the assay wherein the transitions for a particular analyte are only acquired in a time window around the expected retention time, significantly increasing the number of analytes that can be detected and quantified in a single LC-MS experiment and contributing to the selectivity of the test, as retention time is a property dependent on the physical nature of the analyte. A single analyte can also be monitored with more than one transition. Finally, included in the assay can be standards that correspond to the analytes of interest (e.g., same amino acid sequence), but differ by the inclusion of stable isotopes. Stable isotopic standards (SIS) can be incorporated into the assay at precise levels and used to quantify the corresponding unknown analyte. An additional level of specificity is contributed by the co-elution of the unknown analyte and its corresponding SIS and properties of their transitions (e.g., the similarity in the ratio of the level of two transitions of the unknown and the ratio of the two transitions of its corresponding SIS).

[0095] Mass spectrometry assays, instruments and systems suitable for biomarker peptide analysis can include, without limitation, matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) MS; MALDI-TOF post-source-decay (PSD); MALDI-TOF/TOF; surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF) MS; electrospray ionization mass spectrometry (ESI-MS); ESI-MS/MS; ESI-MS/(MS).sub.n (n is an integer greater than zero); ESI 3D or linear (2D) ion trap MS; ESI triple quadrupole MS; ESI quadrupole orthogonal TOF (Q-TOF); ESI Fourier transform MS systems; desorption/ionization on silicon (DIOS); secondary ion mass spectrometry (SIMS); atmospheric pressure chemical ionization mass spectrometry (APCI-MS); APCI-MS/MS; APCI-(MS).sub.n; ion mobility spectrometry (IMS); inductively coupled plasma mass spectrometry (ICP-MS) atmospheric pressure photoionization mass spectrometry (APPI-MS); APPI-MS/MS; and APPI-(MS).sub.n. Peptide ion fragmentation in tandem MS (MS/MS) arrangements can be achieved using manners established in the art, such as, e.g., collision induced dissociation (CID). As described herein, detection and quantification of biomarkers by mass spectrometry can involve multiple reaction monitoring (MRM), such as described among others by Kuhn et al. Proteomics 4: 1175-86 (2004). Scheduled multiple-reaction-monitoring (Scheduled MRM) mode acquisition during LC-MS/MS analysis enhances the sensitivity and accuracy of peptide quantitation. Anderson and Hunter, Molecular and Cellular Proteomics 5(4):573 (2006). As described herein, mass spectrometry-based assays can be advantageously combined with upstream peptide or protein separation or fractionation methods, such as for example with the chromatographic and other methods described herein below. As further described herein, shotgun quantitative proteomics can be combined with SRM/MRM-based assays for high-throughput identification and verification of prognostic biomarkers of preterm birth.

[0096] A person skilled in the art will appreciate that a number of methods can be used to determine the amount of a biomarker, including mass spectrometry approaches, such as MS/MS, LC-MS/MS, multiple reaction monitoring (MRM) or SRM and product-ion monitoring (PIM) and also including antibody based methods such as immunoassays such as Western blots, enzyme-linked immunosorbant assay (ELISA), immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay, dot blotting, and FACS. Accordingly, in some embodiments, determining the level of the at least one biomarker comprises using an immunoassay and/or mass spectrometric methods. In additional embodiments, the mass spectrometric methods are selected from MS, MS/MS, LC-MS/MS, SRM, PIM, and other such methods that are known in the art. In other embodiments, LC-MS/MS further comprises 1D LC-MS/MS, 2D LC-MS/MS or 3D LC-MS/MS. Immunoassay techniques and protocols are generally known to those skilled in the art (Price and Newman, Principles and Practice of Immunoassay, 2nd Edition, Grove's Dictionaries, 1997; and Gosling, Immunoassays: A Practical Approach, Oxford University Press, 2000.) A variety of immunoassay techniques, including competitive and non-competitive immunoassays, can be used (Self et al., Curr. Opin. Biotechnol., 7:60-65 (1996).

[0097] In further embodiments, the immunoassay is selected from Western blot, ELISA, immunoprecipitation, immunohistochemistry, immunofluorescence, radioimmunoassay (MA), dot blotting, and FACS. In certain embodiments, the immunoassay is an ELISA. In yet a further embodiment, the ELISA is direct ELISA (enzyme-linked immunosorbent assay), indirect ELISA, sandwich ELISA, competitive ELISA, multiplex ELISA, ELISPOT technologies, and other similar techniques known in the art. Principles of these immunoassay methods are known in the art, for example John R. Crowther, The ELISA Guidebook, 1st ed., Humana Press 2000, ISBN 0896037282. Typically ELISAs are performed with antibodies but they can be performed with any capture agents that bind specifically to one or more biomarkers of the invention and that can be detected. Multiplex ELISA allows simultaneous detection of two or more analytes within a single compartment (e.g., microplate well) usually at a plurality of array addresses (Nielsen and Geierstanger 2004. J Immunol Methods 290: 107-20 (2004) and Ling et al. 2007. Expert Rev Mol Diagn 7: 87-98 (2007)).

[0098] In some embodiments, Radioimmunoassay (RIA) can be used to detect one or more biomarkers in the methods of the invention. RIA is a competition-based assay that is well known in the art and involves mixing known quantities of radioactively-labelled (e.g., .sup.125I or .sup.131I-labelled) target analyte with antibody specific for the analyte, then adding non-labelled analyte from a sample and measuring the amount of labelled analyte that is displaced (see, e.g., An Introduction to Radioimmunoassay and Related Techniques, by Chard T, ed., Elsevier Science 1995, ISBN 0444821198 for guidance).

[0099] A detectable label can be used in the assays described herein for direct or indirect detection of the biomarkers in the methods of the invention. A wide variety of detectable labels can be used, with the choice of label depending on the sensitivity required, ease of conjugation with the antibody, stability requirements, and available instrumentation and disposal provisions. Those skilled in the art are familiar with selection of a suitable detectable label based on the assay detection of the biomarkers in the methods of the invention. Suitable detectable labels include, but are not limited to, fluorescent dyes (e.g., fluorescein, fluorescein isothiocyanate (FITC), Oregon Green™, rhodamine, Texas red, tetrarhodimine isothiocynate (TRITC), Cy3, Cy5, etc.), fluorescent markers (e.g., green fluorescent protein (GFP), phycoerythrin, etc.), enzymes (e.g., luciferase, horseradish peroxidase, alkaline phosphatase, etc.), nanoparticles, biotin, digoxigenin, metals, and the like.

[0100] For mass-spectrometry based analysis, differential tagging with isotopic reagents, e.g., isotope-coded affinity tags (ICAT) or the more recent variation that uses isobaric tagging reagents, iTRAQ (Applied Biosystems, Foster City, Calif.), or tandem mass tags, TMT, (Thermo Scientific, Rockford, Ill.), followed by multidimensional liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis can provide a further methodology in practicing the methods of the invention.

[0101] A chemiluminescence assay using a chemiluminescent antibody can be used for sensitive, non-radioactive detection of protein levels. An antibody labeled with fluorochrome also can be suitable. Examples of fluorochromes include, without limitation, DAPI, fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin, R-phycoerythrin, rhodamine, Texas red, and lissamine. Indirect labels include various enzymes well known in the art, such as horseradish peroxidase (HRP), alkaline phosphatase (AP), beta-galactosidase, urease, and the like. Detection systems using suitable substrates for horseradish-peroxidase, alkaline phosphatase, beta-galactosidase are well known in the art.

[0102] A signal from the direct or indirect label can be analyzed, for example, using a spectrophotometer to detect color from a chromogenic substrate; a radiation counter to detect radiation such as a gamma counter for detection of .sup.125I; or a fluorometer to detect fluorescence in the presence of light of a certain wavelength. For detection of enzyme-linked antibodies, a quantitative analysis can be made using a spectrophotometer such as an EMAX Microplate Reader (Molecular Devices; Menlo Park, Calif.) in accordance with the manufacturer's instructions. If desired, assays used to practice the invention can be automated or performed robotically, and the signal from multiple samples can be detected simultaneously.

[0103] In some embodiments, the methods described herein encompass quantification of the biomarkers using mass spectrometry (MS). In further embodiments, the mass spectrometry can be liquid chromatography-mass spectrometry (LC-MS), multiple reaction monitoring (MRM) or selected reaction monitoring (SRM). In additional embodiments, the MRM or SRM can further encompass scheduled MRM or scheduled SRM.

[0104] As described above, chromatography can also be used in practicing the methods of the invention. Chromatography encompasses methods for separating chemical substances and generally involves a process in which a mixture of analytes is carried by a moving stream of liquid or gas (“mobile phase”) and separated into components as a result of differential distribution of the analytes as they flow around or over a stationary liquid or solid phase (“stationary phase”), between the mobile phase and said stationary phase. The stationary phase can be usually a finely divided solid, a sheet of filter material, or a thin film of a liquid on the surface of a solid, or the like. Chromatography is well understood by those skilled in the art as a technique applicable for the separation of chemical compounds of biological origin, such as, e.g., amino acids, proteins, fragments of proteins or peptides, etc.

[0105] Chromatography can be columnar (i.e., wherein the stationary phase is deposited or packed in a column), preferably liquid chromatography, and yet more preferably high-performance liquid chromatography (HPLC), or ultra high performance/pressure liquid chromatography (UHPLC). Particulars of chromatography are well known in the art (Bidlingmeyer, Practical HPLC Methodology and Applications, John Wiley & Sons Inc., 1993). Exemplary types of chromatography include, without limitation, high-performance liquid chromatography (HPLC), UHPLC, normal phase HPLC (NP-HPLC), reversed phase HPLC (RP-HPLC), ion exchange chromatography (IEC), such as cation or anion exchange chromatography, hydrophilic interaction chromatography (HILIC), hydrophobic interaction chromatography (HIC), size exclusion chromatography (SEC) including gel filtration chromatography or gel permeation chromatography, chromatofocusing, affinity chromatography such as immuno-affinity, immobilised metal affinity chromatography, and the like. Chromatography, including single-, two- or more-dimensional chromatography, can be used as a peptide fractionation method in conjunction with a further peptide analysis method, such as for example, with a downstream mass spectrometry analysis as described elsewhere in this specification.

[0106] Further peptide or polypeptide separation, identification or quantification methods can be used, optionally in conjunction with any of the above described analysis methods, for measuring biomarkers in the present disclosure. Such methods include, without limitation, chemical extraction partitioning, isoelectric focusing (IEF) including capillary isoelectric focusing (CIEF), capillary isotachophoresis (CITP), capillary electrochromatography (CEC), and the like, one-dimensional polyacrylamide gel electrophoresis (PAGE), two-dimensional polyacrylamide gel electrophoresis (2D-PAGE), capillary gel electrophoresis (CGE), capillary zone electrophoresis (CZE), micellar electrokinetic chromatography (MEKC), free flow electrophoresis (FFE), etc.

[0107] In the context of the invention, the term “capture agent” refers to a compound that can specifically bind to a target, in particular a biomarker. The term includes antibodies, antibody fragments, nucleic acid-based protein binding reagents (e.g. aptamers, Slow Off-rate Modified Aptamers (SOMAmer™)), protein-capture agents, natural ligands (i.e. a hormone for its receptor or vice versa), small molecules or variants thereof.

[0108] Capture agents can be configured to specifically bind to a target, in particular a biomarker. Capture agents can include but are not limited to organic molecules, such as polypeptides, polynucleotides and other non polymeric molecules that are identifiable to a skilled person. In the embodiments disclosed herein, capture agents include any agent that can be used to detect, purify, isolate, or enrich a target, in particular a biomarker. Any art-known affinity capture technologies can be used to selectively isolate and enrich/concentrate biomarkers that are components of complex mixtures of biological media for use in the disclosed methods.

[0109] Antibody capture agents that specifically bind to a biomarker can be prepared using any suitable methods known in the art. See, e.g., Coligan, Current Protocols in Immunology (1991); Harlow & Lane, Antibodies: A Laboratory Manual (1988); Goding, Monoclonal Antibodies: Principles and Practice (2d ed. 1986). Antibody capture agents can be any immunoglobulin or derivative thereof, whether natural or wholly or partially synthetically produced. All derivatives thereof which maintain specific binding ability are also included in the term. Antibody capture agents have a binding domain that is homologous or largely homologous to an immunoglobulin binding domain and can be derived from natural sources, or partly or wholly synthetically produced. Antibody capture agents can be monoclonal or polyclonal antibodies. In some embodiments, an antibody is a single chain antibody. Those of ordinary skill in the art will appreciate that antibodies can be provided in any of a variety of forms including, for example, humanized, partially humanized, chimeric, chimeric humanized, etc. Antibody capture agents can be antibody fragments including, but not limited to, Fab, Fab′, F(ab′)2, scFv, Fv, dsFv diabody, and Fd fragments. An antibody capture agent can be produced by any means. For example, an antibody capture agent can be enzymatically or chemically produced by fragmentation of an intact antibody and/or it can be recombinantly produced from a gene encoding the partial antibody sequence. An antibody capture agent can comprise a single chain antibody fragment. Alternatively or additionally, antibody capture agent can comprise multiple chains which are linked together, for example, by disulfide linkages; and, any functional fragments obtained from such molecules, wherein such fragments retain specific-binding properties of the parent antibody molecule. Because of their smaller size as functional components of the whole molecule, antibody fragments can offer advantages over intact antibodies for use in certain immunochemical techniques and experimental applications.

[0110] Suitable capture agents useful for practicing the invention also include aptamers. Aptamers are oligonucleotide sequences that can bind to their targets specifically via unique three dimensional (3-D) structures. An aptamer can include any suitable number of nucleotides and different aptamers can have either the same or different numbers of nucleotides. Aptamers can be DNA or RNA or chemically modified nucleic acids and can be single stranded, double stranded, or contain double stranded regions, and can include higher ordered structures. An aptamer can also be a photoaptamer, where a photoreactive or chemically reactive functional group is included in the aptamer to allow it to be covalently linked to its corresponding target. Use of an aptamer capture agent can include the use of two or more aptamers that specifically bind the same biomarker. An aptamer can include a tag. An aptamer can be identified using any known method, including the SELEX (systematic evolution of ligands by exponential enrichment), process. Once identified, an aptamer can be prepared or synthesized in accordance with any known method, including chemical synthetic methods and enzymatic synthetic methods and used in a variety of applications for biomarker detection. Liu et al., Curr Med Chem. 18(27):4117-25 (2011). Capture agents useful in practicing the methods of the invention also include SOMAmers (Slow Off-Rate Modified Aptamers) known in the art to have improved off-rate characteristics. Brody et al., J Mol Biol. 422(5):595-606 (2012). SOMAmers can be generated using any known method, including the SELEX method.

[0111] It is understood by those skilled in the art that biomarkers can be modified prior to analysis to improve their resolution or to determine their identity. For example, the biomarkers can be subject to proteolytic digestion before analysis. Any protease can be used. Proteases, such as trypsin, that are likely to cleave the biomarkers into a discrete number of fragments are particularly useful. The fragments that result from digestion function as a fingerprint for the biomarkers, thereby enabling their detection indirectly. This is particularly useful where there are biomarkers with similar molecular masses that might be confused for the biomarker in question. Also, proteolytic fragmentation is useful for high molecular weight biomarkers because smaller biomarkers are more easily resolved by mass spectrometry. In another example, biomarkers can be modified to improve detection resolution. For instance, neuraminidase can be used to remove terminal sialic acid residues from glycoproteins to improve binding to an anionic adsorbent and to improve detection resolution. In another example, the biomarkers can be modified by the attachment of a tag of particular molecular weight that specifically binds to molecular biomarkers, further distinguishing them. Optionally, after detecting such modified biomarkers, the identity of the biomarkers can be further determined by matching the physical and chemical characteristics of the modified biomarkers in a protein database (e.g., SwissProt).

[0112] It is further appreciated in the art that biomarkers in a sample can be captured on a substrate for detection. Traditional substrates include antibody-coated 96-well plates or nitrocellulose membranes that are subsequently probed for the presence of the proteins. Alternatively, protein-binding molecules attached to microspheres, microparticles, microbeads, beads, or other particles can be used for capture and detection of biomarkers. The protein-binding molecules can be antibodies, peptides, peptoids, aptamers, small molecule ligands or other protein-binding capture agents attached to the surface of particles. Each protein-binding molecule can include unique detectable label that is coded such that it can be distinguished from other detectable labels attached to other protein-binding molecules to allow detection of biomarkers in multiplex assays. Examples include, but are not limited to, color-coded microspheres with known fluorescent light intensities (see e.g., microspheres with xMAP technology produced by Luminex (Austin, Tex.); microspheres containing quantum dot nanocrystals, for example, having different ratios and combinations of quantum dot colors (e.g., Qdot nanocrystals produced by Life Technologies (Carlsbad, Calif.); glass coated metal nanoparticles (see e.g., SERS nanotags produced by Nanoplex Technologies, Inc. (Mountain View, Calif.); barcode materials (see e.g., sub-micron sized striped metallic rods such as Nanobarcodes produced by Nanoplex Technologies, Inc.), encoded microparticles with colored bar codes (see e.g., CellCard produced by Vitra Bioscience, vitrabio.com), glass microparticles with digital holographic code images (see e.g., CyVera microbeads produced by Illumina (San Diego, Calif.); chemiluminescent dyes, combinations of dye compounds; and beads of detectably different sizes.

[0113] In another aspect, biochips can be used for capture and detection of the biomarkers of the invention. Many protein biochips are known in the art. These include, for example, protein biochips produced by Packard BioScience Company (Meriden Conn.), Zyomyx (Hayward, Calif.) and Phylos (Lexington, Mass.). In general, protein biochips comprise a substrate having a surface. A capture reagent or adsorbent is attached to the surface of the substrate. Frequently, the surface comprises a plurality of addressable locations, each of which location has the capture agent bound there. The capture agent can be a biological molecule, such as a polypeptide or a nucleic acid, which captures other biomarkers in a specific manner. Alternatively, the capture agent can be a chromatographic material, such as an anion exchange material or a hydrophilic material. Examples of protein biochips are well known in the art.

[0114] Measuring mRNA in a biological sample can be used as a surrogate for detection of the level of the corresponding protein biomarker in a biological sample. Thus, any of the biomarkers or biomarker panels described herein can also be detected by detecting the appropriate RNA. Levels of mRNA can measured by reverse transcription quantitative polymerase chain reaction (RT-PCR followed with qPCR). RT-PCR is used to create a cDNA from the mRNA. The cDNA can be used in a qPCR assay to produce fluorescence as the DNA amplification process progresses. By comparison to a standard curve, qPCR can produce an absolute measurement such as number of copies of mRNA per cell. Northern blots, microarrays, Invader assays, and RT-PCR combined with capillary electrophoresis have all been used to measure expression levels of mRNA in a sample. See Gene Expression Profiling: Methods and Protocols, Richard A. Shimkets, editor, Humana Press, 2004.

[0115] Some embodiments disclosed herein relate to diagnostic and prognostic methods of determining the probability for preterm birth in a pregnant female. The detection of the level of expression of one or more biomarkers and/or the determination of a ratio of biomarkers can be used to determine the probability for preterm birth in a pregnant female. Such detection methods can be used, for example, for early diagnosis of the condition, to determine whether a subject is predisposed to preterm birth, to monitor the progress of preterm birth or the progress of treatment protocols, to assess the severity of preterm birth, to forecast the outcome of preterm birth and/or prospects of recovery or birth at full term, or to aid in the determination of a suitable treatment for preterm birth.

[0116] The quantitation of biomarkers in a biological sample can be determined, without limitation, by the methods described above as well as any other method known in the art. The quantitative data thus obtained is then subjected to an analytic classification process. In such a process, the raw data is manipulated according to an algorithm, where the algorithm has been pre-defined by a training set of data, for example as described in the examples provided herein. An algorithm can utilize the training set of data provided herein, or can utilize the guidelines provided herein to generate an algorithm with a different set of data.

[0117] In some embodiments, analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses the use of a predictive model. In further embodiments, analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses comparing said measurable feature with a reference feature. As those skilled in the art can appreciate, such comparison can be a direct comparison to the reference feature or an indirect comparison where the reference feature has been incorporated into the predictive model. In further embodiments, analyzing a measurable feature to determine the probability for preterm birth in a pregnant female encompasses one or more of a linear discriminant analysis model, a support vector machine classification algorithm, a recursive feature elimination model, a prediction analysis of microarray model, a logistic regression model, a CART algorithm, a flex tree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, a machine learning algorithm, a penalized regression method, or a combination thereof. In particular embodiments, the analysis comprises logistic regression.

[0118] An analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample. Examples of useful methods include linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, machine learning algorithms; etc.

[0119] For creation of a random forest for prediction of GAB one skilled in the art can consider a set of k subjects (pregnant women) for whom the gestational age at birth (GAB) is known, and for whom N analytes (transitions) have been measured in a blood specimen taken several weeks prior to birth. A regression tree begins with a root node that contains all the subjects. The average GAB for all subjects can be calculated in the root node. The variance of the GAB within the root node will be high, because there is a mixture of women with different GAB's. The root node is then divided (partitioned) into two branches, so that each branch contains women with a similar GAB. The average GAB for subjects in each branch is again calculated. The variance of the GAB within each branch will be lower than in the root node, because the subset of women within each branch has relatively more similar GAB's than those in the root node. The two branches are created by selecting an analyte and a threshold value for the analyte that creates branches with similar GAB. The analyte and threshold value are chosen from among the set of all analytes and threshold values, usually with a random subset of the analytes at each node. The procedure continues recursively producing branches to create leaves (terminal nodes) in which the subjects have very similar GAB's. The predicted GAB in each terminal node is the average GAB for subjects in that terminal node. This procedure creates a single regression tree. A random forest can consist of several hundred or several thousand such trees.

[0120] Classification can be made according to predictive modeling methods that set a threshold for determining the probability that a sample belongs to a given class. The probability preferably is at least 50%, or at least 60%, or at least 70%, or at least 80% or higher. Classifications also can be made by determining whether a comparison between an obtained dataset and a reference dataset yields a statistically significant difference. If so, then the sample from which the dataset was obtained is classified as not belonging to the reference dataset class. Conversely, if such a comparison is not statistically significantly different from the reference dataset, then the sample from which the dataset was obtained is classified as belonging to the reference dataset class.

[0121] The predictive ability of a model can be evaluated according to its ability to provide a quality metric, e.g. AUROC (area under the ROC curve) or accuracy, of a particular value, or range of values. Area under the curve measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest. In some embodiments, a desired quality threshold is a predictive model that will classify a sample with an accuracy of at least about 0.5, at least about 0.55, at least about 0.6, at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, at least about 0.95, or higher. As an alternative measure, a desired quality threshold can refer to a predictive model that will classify a sample with an AUC of at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

[0122] As is known in the art, the relative sensitivity and specificity of a predictive model can be adjusted to favor either the selectivity metric or the sensitivity metric, where the two metrics have an inverse relationship. The limits in a model as described above can be adjusted to provide a selected sensitivity or specificity level, depending on the particular requirements of the test being performed. One or both of sensitivity and specificity can be at least about 0.7, at least about 0.75, at least about 0.8, at least about 0.85, at least about 0.9, or higher.

[0123] The raw data can be initially analyzed by measuring the values for each biomarker, usually in triplicate or in multiple triplicates. The data can be manipulated, for example, raw data can be transformed using standard curves, and the average of triplicate measurements used to calculate the average and standard deviation for each patient. These values can be transformed before being used in the models, e.g. log-transformed, Box-Cox transformed (Box and Cox, Royal Stat. Soc., Series B, 26:211-246(1964). The data are then input into a predictive model, which will classify the sample according to the state. The resulting information can be communicated to a patient or health care provider.

[0124] To generate a predictive model for preterm birth, a robust data set, comprising known control samples and samples corresponding to the preterm birth classification of interest is used in a training set. A sample size can be selected using generally accepted criteria. As discussed above, different statistical methods can be used to obtain a highly accurate predictive model. Examples of such analysis are provided in Example 2.

[0125] In one embodiment, hierarchical clustering is performed in the derivation of a predictive model, where the Pearson correlation is employed as the clustering metric. One approach is to consider a preterm birth dataset as a “learning sample” in a problem of “supervised learning.” CART is a standard in applications to medicine (Singer, Recursive Partitioning in the Health Sciences, Springer (1999)) and can be modified by transforming any qualitative features to quantitative features; sorting them by attained significance levels, evaluated by sample reuse methods for Hotelling's T.sup.2 statistic; and suitable application of the lasso method. Problems in prediction are turned into problems in regression without losing sight of prediction, indeed by making suitable use of the Gini criterion for classification in evaluating the quality of regressions.

[0126] This approach led to what is termed FlexTree (Huang, Proc. Nat. Acad. Sci. U.S.A 101:10529-10534(2004)). FlexTree performs very well in simulations and when applied to multiple forms of data and is useful for practicing the claimed methods. Software automating FlexTree has been developed. Alternatively, LARTree or LART can be used (Turnbull (2005) Classification Trees with Subset Analysis Selection by the Lasso, Stanford University). The name reflects binary trees, as in CART and FlexTree; the lasso, as has been noted; and the implementation of the lasso through what is termed LARS by Efron et al. (2004) Annals of Statistics 32:407-451 (2004). See, also, Huang et al., Proc. Natl. Acad. Sci. USA. 101(29):10529-34 (2004). Other methods of analysis that can be used include logic regression. One method of logic regression Ruczinski, Journal of Computational and Graphical Statistics 12:475-512 (2003). Logic regression resembles CART in that its classifier can be displayed as a binary tree. It is different in that each node has Boolean statements about features that are more general than the simple “and” statements produced by CART.

[0127] Another approach is that of nearest shrunken centroids (Tibshirani, Proc. Natl. Acad. Sci. U.S.A 99:6567-72(2002)). The technology is k-means-like, but has the advantage that by shrinking cluster centers, one automatically selects features, as is the case in the lasso, to focus attention on small numbers of those that are informative. The approach is available as PAM software and is widely used. Two further sets of algorithms that can be used are random forests (Breiman, Machine Learning 45:5-32 (2001)) and MART (Hastie, The Elements of Statistical Learning, Springer (2001)). These two methods are known in the art as “committee methods,” that involve predictors that “vote” on outcome.

[0128] To provide significance ordering, the false discovery rate (FDR) can be determined. First, a set of null distributions of dissimilarity values is generated. In one embodiment, the values of observed profiles are permuted to create a sequence of distributions of correlation coefficients obtained out of chance, thereby creating an appropriate set of null distributions of correlation coefficients (Tusher et al., Proc. Natl. Acad. Sci. U.S.A 98, 5116-21 (2001)). The set of null distribution is obtained by: permuting the values of each profile for all available profiles; calculating the pair-wise correlation coefficients for all profile; calculating the probability density function of the correlation coefficients for this permutation; and repeating the procedure for N times, where N is a large number, usually 300. Using the N distributions, one calculates an appropriate measure (mean, median, etc.) of the count of correlation coefficient values that their values exceed the value (of similarity) that is obtained from the distribution of experimentally observed similarity values at given significance level.

[0129] The FDR is the ratio of the number of the expected falsely significant correlations (estimated from the correlations greater than this selected Pearson correlation in the set of randomized data) to the number of correlations greater than this selected Pearson correlation in the empirical data (significant correlations). This cut-off correlation value can be applied to the correlations between experimental profiles. Using the aforementioned distribution, a level of confidence is chosen for significance. This is used to determine the lowest value of the correlation coefficient that exceeds the result that would have obtained by chance. Using this method, one obtains thresholds for positive correlation, negative correlation or both. Using this threshold(s), the user can filter the observed values of the pair wise correlation coefficients and eliminate those that do not exceed the threshold(s). Furthermore, an estimate of the false positive rate can be obtained for a given threshold. For each of the individual “random correlation” distributions, one can find how many observations fall outside the threshold range. This procedure provides a sequence of counts. The mean and the standard deviation of the sequence provide the average number of potential false positives and its standard deviation.

[0130] In an alternative analytical approach, variables chosen in the cross-sectional analysis are separately employed as predictors in a time-to-event analysis (survival analysis), where the event is the occurrence of preterm birth, and subjects with no event are considered censored at the time of giving birth. Given the specific pregnancy outcome (preterm birth event or no event), the random lengths of time each patient will be observed, and selection of proteomic and other features, a parametric approach to analyzing survival can be better than the widely applied semi-parametric Cox model. A Weibull parametric fit of survival permits the hazard rate to be monotonically increasing, decreasing, or constant, and also has a proportional hazards representation (as does the Cox model) and an accelerated failure-time representation. All the standard tools available in obtaining approximate maximum likelihood estimators of regression coefficients and corresponding functions are available with this model.

[0131] In addition the Cox models can be used, especially since reductions of numbers of covariates to manageable size with the lasso will significantly simplify the analysis, allowing the possibility of a nonparametric or semi-parametric approach to prediction of time to preterm birth. These statistical tools are known in the art and applicable to all manner of proteomic data. A set of biomarker, clinical and genetic data that can be easily determined, and that is highly informative regarding the probability for preterm birth and predicted time to a preterm birth event in said pregnant female is provided. Also, algorithms provide information regarding the probability for preterm birth in the pregnant female.

[0132] Accordingly, one skilled in the art understands that the probability for preterm birth according to the invention can be determined using either a quantitative or a categorical variable. For example, in practicing the methods of the invention the measurable feature of each of N biomarkers can be subjected to categorical data analysis to determine the probability for preterm birth as a binary categorical outcome. Alternatively, the methods of the invention may analyze the measurable feature of each of N biomarkers by initially calculating quantitative variables, in particular, predicted gestational age at birth. The predicted gestational age at birth can subsequently be used as a basis to predict risk of preterm birth. By initially using a quantitative variable and subsequently converting the quantitative variable into a categorical variable the methods of the invention take into account the continuum of measurements detected for the measurable features. For example, by predicting the gestational age at birth rather than making a binary prediction of preterm birth versus term birth, it is possible to tailor the treatment for the pregnant female. For example, an earlier predicted gestational age at birth will result in more intensive prenatal intervention, i.e. monitoring and treatment, than a predicted gestational age that approaches full term.

[0133] Among women with a predicted GAB of j days plus or minus k days, p(PTB) can estimated as the proportion of women in the PAPR clinical trial (see Example 1) with a predicted GAB of j days plus or minus k days who actually deliver before 37 weeks gestational age. More generally, for women with a predicted GAB of j days plus or minus k days, the probability that the actual gestational age at birth will be less than a specified gestational age, p(actual GAB<specified GAB), was estimated as the proportion of women in the PAPR clinical trial with a predicted GAB of j days plus or minus k days who actually deliver before the specified gestational age.

[0134] In the development of a predictive model, it can be desirable to select a subset of markers, i.e. at least 3, at least 4, at least 5, at least 6, up to the complete set of markers. Usually a subset of markers will be chosen that provides for the needs of the quantitative sample analysis, e.g. availability of reagents, convenience of quantitation, etc., while maintaining a highly accurate predictive model. The selection of a number of informative markers for building classification models requires the definition of a performance metric and a user-defined threshold for producing a model with useful predictive ability based on this metric. For example, the performance metric can be the AUC, the sensitivity and/or specificity of the prediction as well as the overall accuracy of the prediction model.

[0135] As will be understood by those skilled in the art, an analytic classification process can use any one of a variety of statistical analytic methods to manipulate the quantitative data and provide for classification of the sample. Examples of useful methods include, without limitation, linear discriminant analysis, recursive feature elimination, a prediction analysis of microarray, a logistic regression, a CART algorithm, a FlexTree algorithm, a LART algorithm, a random forest algorithm, a MART algorithm, and machine learning algorithms.

[0136] As described in Example 2, various methods are used in a training model. The selection of a subset of markers can be for a forward selection or a backward selection of a marker subset. The number of markers can be selected that will optimize the performance of a model without the use of all the markers. One way to define the optimum number of terms is to choose the number of terms that produce a model with desired predictive ability (e.g. an AUC>0.75, or equivalent measures of sensitivity/specificity) that lies no more than one standard error from the maximum value obtained for this metric using any combination and number of terms used for the given algorithm.

TABLE-US-00001 TABLE 1 Transitions with p-values less than 0.05 in univariate Cox Proportional Hazards analyses to predict Gestational Age at Birth p-value Cox Transition Protein univariate ITLPDFTGDLR_ LBP_HUMAN 0.006 624.34_920.4 ELLESYIDGR_ THRB_HUMAN 0.006 597.8_710.3 TDAPDLPEENQAR_ CO5_HUMAN 0.007 728.34_613.3 AFTECCVVASQLR_ CO5_HUMAN 0.009 770.87_574.3 SFRPFVPR_ LBP_HUMAN 0.011 335.86_272.2 ITLPDFTGDLR_ LBP_HUMAN 0.012 624.34_288.2 SFRPF_ LBP_HUMAN 0.015 VPR_ 335.86_63_5.3 ELLESYIDGR_ THRB_HUMAN 0.018 597.8_839.4 LEQGENVFLQATDK_ C1QB_HUMAN 0.019 796.4_822.4 ETAASLLQAGYK_ THRB_HUMAN 0.021 626.33_679.4 VTGWGNLK_ THRB_HUMAN 0.021 437.74_617.3 EAQLPV1ENK_ PLMN_HUMAN 0.023 570.82_699.4 EAQLP_ PLMN_HUMAN 0.023 VIENK_ 570.82_329.1 FLQEQGHR_ CO8G_HUMAN 0.025 338.84_497.3 IRPFFPQQ_ FIBB_HUMAN 0.028 516.79_661.4 ETAASLLQAGYK_ THRB_HUMAN 0.029 626.33_879.5 AFTECCVVASQLR_ CO5_HUMAN 0.030 770.87_673.4 TLLPVSKPEIR_ CO5_HUMAN 0.030 418.26_288.2 LSSPAVITDK_ PLMN_HUMAN 0.033 515.79_743.4 YEVQGEVFTKPQLWP_ CRP_HUMAN 0.036 910.96_392.2 LQGTLPVEAR_ CO5_HUMAN 0.036 542.31_571.3 VRPQQLVK_ ITIH4_HUMAN 0.036 484.31_609.3 IEEIAAK_ CO5_HUMAN 0.041 387.22_531.3 TLLPVSKPEIR_ CO5__HUMAN 0.042 418.26_514.3 VQEAHLTEDQIFYFPK_ CO8G_HUMAN 0.047 655.66_701.4 ISLLLIESWLEPVR_ CSH_HUMAN 0.048 834.49_371.2 ALQDQLVLVAAK_ ANGT_HUMAN 0.048 634.88_289.2 YEFLNGR_ PLMN_HUMAN 0.049 449.72_293.1

TABLE-US-00002 TABLE 2 Transitions selected by the Cox stepwise AIC analysis Transition coef exp(coef) se(coef) z Pr(>|z|) Collection.Window.  1.28E−01 1.14E+00 2.44E−02 5.26 1.40E−07 GA.in.Days ITLPDFTGDLR_  2.02E+00 7.52E+00 1.14E+00 1.77 0.07667 624.34_920.4 TPSAAYLWVGTGASEAEK  2.85E+01 2.44E+12 3.06E+00 9.31 <2e−16 919.45_849.4 TATSEYQTFFNPR_  5.14E+00 1.70E+02 6.26E−01 8.21 2.20E−16 781.37_386.2 TASDFITK_ −1.25E+00 2.86E−01 1.58E+00 −0.79 0.42856 441.73_781.4 IITGLLEFEVYLEYLQNR_  1.30E+01 4.49E+05 1.45E+00 9 <2e−16 738.4_530.3 IIGGSDADIK_ −6.43E+01 1.16E−28 6.64E+00 −9.68 <2e−16 494.77_762.4 YTTEIIK_  6.96E+01 1.75E+30 7.06E+00 9.86 <2e−16 434.25_603.4 EDTPNSVWEPAK_  7.91E+00 2.73E+03 2.66E+00 2.98 0.00293 686.82_3 15.2 LYYGDDEK_  8.74E+00 6.23E+03 1.57E+00 5.57 2.50E−08 501.72_726.3 VRPQQLVK_  4.64E+01 1.36E+20 3.97E+00 11.66 <2e−16 484.31_609.3 GGEIEGFR_ −3.33E+00 3.57E−02 2.19E+00 −1.52 0.12792 432.71_379.2 DGSPDVTTADIGANTP −1.52E+01 2.51E−07 1.41E+00 −10.8 <2e−16 DATK_973.45_844.4 VQEAHLTEDQIFYFPK_ −2.02E+01 1.77E−09 2.45E+00 −8.22 2.20E−16 655.66_391.2 VEIDTK_  7.06E+00 1.17E+03 1.45E+00 4.86 1.20E−06 352.7_476.3 AVLTIDEK_  7.85E+00 2.56E+03 9.46E−01 8.29 <2e−16 444.76_605.3 FSVVYAK_ −2.44E+01 2.42E−11 3.08E+00 −7.93 2.20E−15 407.23_579.4 YYLQGAK_ −1.82E+01 1.22E−08 2.45E+00 −7.44 1.00E−13 421.72_516.3 EENFYVDETTVVK_ −1.90E+01 5.36E−09 2.71E+00 −7.03 2.00E−12 786.88_259.1 YGFYTHVFR_  1.90E+01 1.71E+08 2.73E+00 6.93 4.20E−12 397.2_421.3 HTLNQIDEVK_  1.03E+01 3.04E+04 2.11E+00 4.89 9.90E−07 598.82_951.5 AFIQLWAFDAVK_  1.08E+01 4.72E+04 2.59E+00 4.16 3.20E−05 704.89_836.4 SGFSFGFK43_8.72_  1.35E+01 7.32E+05 2.56E+00 5.27 1.40E−07 585.3 GWVTDGFSSLK_ −3.12E+00 4.42E−02 9.16E−01 −3.4 0.00066 598.8_854.4 ITENDIQIALDDAK_  1.91E+00 6.78E+00 1.36E+00 1.4 0.16036 779.9_632.3

TABLE-US-00003 TABLE 3 Transitions selected by Cox lasso model exp se Pr Transition coef (coef) (coef) z (>|z|) Collection. 0.0233 1.02357 0.00928 2.51 0.012 Window.GA. in.Days AFTECCVVAS 1.07568 2.93198 0.84554 1.27 0.203 QLR_ 770.87_574.3 ELLESYIDGR_ 1.3847 3.99365 0.70784 1.96 0.05 597.8_710.3 ITLPDFTGDLR_ 0.814 2.25691 0.40652 2 0.045 624.34_920.4

TABLE-US-00004 TABLE 4 Area under the ROC (AUROC) curve for individual analytes to pre-term  birth subjects from non-pre-term  birth subjects. The 77 transitions discriminate with the highest  AUROC are aare shown. Transition AUROC ELLES_YIDGR_597.8_710.3 0.71 AFTECCWASQLR_770.87_574.3 0.70 ITLPDFTGDLR_624.34_920.4 0.70 IRPFFPQQ_516.79_661.4 0.68 TDAPDLPEENQ_AR_728.34_613.3 0.67 ITLPDFTGDLR_624.34_288.2 0.67 ELLESYIDGR_597.8_839.4 0.67 SFRPFVPR_3_35.86_635.3 0.67 ETAASLLQAGYK_626.33_879.5 0.67 TLLPVSKPEIR_418.26_288.2 0.66 ETAASLLQAGYK_626.33_679.4 0.66 SFRPFVPR_335.86_272.2 0.66 LQGTLP_VEAR542.31_571.3 0.66 VEPLYELVTATDFAYSSTV 0.66 R_754.38_712.4 DPDQTDGLGLSYLSSHIANVE 0.66 R_796.39_328.1 VTGWGNLK_437.74_617.3 0.65 ALQDQLVLVAAK_634.88_289.2 0.65 EAQLPVTENK_570.82_329.1 0.65 VRPQQLVK_484.31_609.3 0.65 AFTECCWASQLR_770.87_673.4 0.65 YEFLNGR_449.72_293.1 0.65 VGEYSLYIGR_578.8_871.5 0.64 EAQLPVIENK_570.82_699.4 0.64 TLLPVSKPEIR_418.26_514.3 0.64 IEEIAAK_387.22_531.3 0.64 LEQGENVFLQATDK_796.4_822.4 0.64 LQGTLPVEAR_542.31_842.5 0.64 FLQEQGHR_338.84_497.3 0.63 ISLLLIESWLEPVR_834.49_371.2 0.63 IITGLLEFEVYLEYLQNR_738.4_530.3 0.63 LSSPAVITDK515.79_743.4 0.63 VRPQQLVK_484.31_722.4 0.63 SLPVSDSVLSGFEQR_810.92_723.3 0.63 VQEAHLTEDQIFYFPK_655.66_701.4 0.63 NADYSYSVWK_616.78_333.2 0.63 DAQYAPGYDK_564.25_813.4 0.62 FQLPGQK_409.23_276.1 0.62 TASDFITK_441.73_781.4 0.62 YGLVTYATYPK_638.33_334.2 0.62 GSFALSFPVESDVAPIAR_931.99_363.2 0.62 TLLIANETLR_572.34_703.4 0.62 VILGAHQEVNLEPFIVQEIEVS 0.62 R_832.78_860.4 TATSEYQTFFNPR_781.37_386.2 0.62 YEVQGEVFTKPQLWP_910.96_392.2 0.62 DISEVVTPR_508.27_472.3 0.62 GSFALSFPVESDVAPIAR_931.99_456.3 0.62 YGFYTHVFR_397.2_421.3 0.62 TLEAQLTPR_514.79_685.4 0.62 YGFYTHVFR_397.2_659.4 0.62 AVGYLITGYQR_620.84_737.4 0.61 DPDQTDGLGLSYLSSHIAN 0.61 VER_796.39_456.2 FNAVLTNPQGDYDTSTGK_964.46_262.1 0.61 SPEQQETVLDGNLIIR_906.48_685.4 0.61 ALNFELPLEYNSALYSR_620.99_538.3 0.61 GGEIEGFR_432.71_508.3 0.61 GIVEECCFR_585.26_900.3 0.61 DAQYAPGYDK_564.25_315.1 0.61 FAFNLYR_465.75_712.4 0.61 YTTEIIK_434.25_603.4 0.61 AVLTIDEK_444.76_605.3 0.61 AITPPHPASQANIIFDITEG 0.60 NLR_825.77_459.3 EPGLCTWQSLR_673.83_790.4 0.60 AVYEAVLR_460.76_587.4 0.60 ALQDQLVLVAAK_634.88_956.6 0.60 AWVAWR_394.71_531.3 0.60 TNLESILSYPK_632.84_807.5 0.60 HLSLLTTLSNR_418.91_376.2 0.60 FTFTLHLETPKPSISSSNLNP 0.60 R_829.44_787.4 AVGYLITGYQR_620.84_523.3 0.60 FQLPGQK_409.23_429.2 0.60 YGLVTYATYPK_638.33_843.4 0.60 TELRPGETLNVNFLLR_624.68_662.4 0.60 LSSPAVITDK_515.79_830.5 0.60 TATSEYQTFFNPR_781.37_272.2 0.60 LPTAVVPLR_483.31_385.3 0.60 APLTKPLK_289.86_260.2 0.60

TABLE-US-00005 TABLE 5 AUROCs for random forest, boosting, lasso, and logistic regression models for a specific number of transitions permitted in the model, as estimated by 100 rounds of bootstrap resampling. Number of transitions rf boosting logit lasso 1 0.59 0.67 0.64 0.69 2 0.66 0.70 0.63 0.68 3 0.69 0.70 0.58 0.71 4 0.68 0.72 0.58 0.71 5 0.73 0.71 0.58 0.68 6 0.72 0.72 0.56 0.68 7 0.74 0.70 0.60 0.67 8 0.73 0.72 0.62 0.67 9 0.72 0.72 0.60 0.67 10 0.74 0.71 0.62 0.66 11 0.73 0.69 0.58 0.67 12 0.73 0.69 0.59 0.66 13 0.74 0.71 0.57 0.66 14 0.73 0.70 0.57 0.65 15 0.72 0.70 0.55 0.64

TABLE-US-00006 TABLE 6 Top 15 transitions selected by each multivariate method, ranked by importance for that method. rf boosting lasso logit 1 ELLES AFTEC AFTEC ALQDQ YIDGR_ CVVAS CVVAS LVLVA 597.8_ QLR_ QLR_ AK_ 710.3 770.87_ 770.87_ 634.88_ 574.3 574.3 289.2 2 TATSE DPDQT ISLLL AVLTI YQTFF DGLGL IESWL DEK_ NPR_ SYLSS EPVR_ 444.76_ 781.37_ HIANV 834.49_ 605.3 386.2 ER_ 371.2 796.39_ 328.1 3 ITLPD ELLES LPTAV Collection. FTGDL YIDGR_ VPLR_ Window. R_ 597.8_ 483.31_ GA.in.Days 624.34_ 710.3 385.3 920.4 4 AFTEC TATSE ALQDQ AHYDL CVVAS YQTFF LVLVA R_ QLR_ NPR_ AK_ 387.7_ 770.87_ 781.37_ 634.88_ 566.3 574.3 386.2 289.2 5 VEPLY ITLPD ETAAS AEAQA ELVTA FTGDL LLQAG QYSAA TDFAY R_ YK_ VAK_ SSTVR_ 624.34_ 626.33_ 654.33_ 754.38_ 920.4 679.4 908.5 712.4 6 GSFAL GGEIE IITGL AEAQA SFPVE GFR_ LEFEV QYSAA SDVAP 432.71_ YLEYL VAK_ IAR_ 379.2 QNR_ 654.33_ 931.99_ 738.4_ 709.4 363.2 530.3 7 VGEYS ALQDQ ADSQA ADSQA LYIGR_ LVLVA QLLLS QLLLS 578.8_ AK_ TVVGV TVVGV 871.5 634.88_ FTAPG FTAPG 289.2 LHLK_ LHLK_ 822.46_ 822.46_ 983.6 983.6 8 SFRPF VGEYS SLPVS AITPP VPR_ LYIGR_ DSVLS HPASQ 33586_ 578.8_ GFEQR_ ANIIF 635.3 871.5 810.92_ DITEG 723.3 NLR_ 825.77_ 459.3 9 ALQDQ VEPLY SFRPF ADSQA LVLVA ELVTA VPR_ QLLLS AK_ TDFAY 335.86_ TVVGV 634.88_ SSTVR_ 272.2 FTAPG 289.2 754.38_ LHLK_ 712.4 822.46_ 664.4 10 EDTPN SPEQQ IIGGS AYSDL SVWEP ETVLD DADIK_ SR_ AK_ GNLII 494.77_ 406.2_ 686.82_ R_ 260.2 375.2 315.2 906.48_ 685.4 11 YGFYT YEFLN NADYS DALSS HVFR_ GR_ YSVWK_ VQESQ 397.2_ 449.72_ 616.78_ VAQQA 421.3 293.1 333.2 R_ 572.96_ 672.4 12 DPDQT LEQGE GSFAL ANRPF DGLGL NVFLQ SFPVE LVFIR_ SYLSS ATDK_ SDVAP 411.58_ HIANV 796.4_ IAR_ 435.3 ER_ 822.4 931_ 796.39_ 99_ 328.1 456.3 13 LEQGE LQGTL LSSPA DALSS NVFLQ PVEAR_ VITDK_ VQESQ ATDK_ 542.31_ 515.79_ VAQQA 796.4_ 571.3 743.4 R_ 822.4 572.96_ 502.3 14 LQGTL ISLLL ELPEH ALEQD PVEAR_ IESWL TVK_ LPVNI 542.31_ EPVR_ 476.76_ K_ 571.3 834.49_ 347.2 620.35_ 371.2 570.4 15 SFRPF TASDF EAQLP AVLTI VPR_ ITK_ VIENK_ DEK_ 335.86_ 441.73_ 570.82_ 444.76_ 272.2 781.4 699.4 718.4

[0137] In yet another aspect, the invention provides kits for determining probability of preterm birth, wherein the kits can be used to detect N of the isolated biomarkers listed in Tables 1 through 63. For example, the kits can be used to detect one or more, two or more, or three of the isolated biomarkers selected from the group consisting of AFTECCVVASQLR, ELLESYIDGR, and ITLPDFTGDLR. For example, the kits can be used to detect one or more, two or more, or three of the isolated biomarkers selected from the group consisting of FLNWIK, FGFGGSTDSGPIR, LLELTGPK, VEHSDLSFSK, IEGNLIFDPNNYLPK, ALVLELAK, TQILEWAAER, DVLLLVHNLPQNLPGYFWYK, SEPRPGVLLR, ITQDAQLK, ALDLSLK, WWGGQPLWITATK, and LSETNR.

[0138] In another aspect, the kits can be used to detect one or more, two or more, three or more, four or more, five or more, six or more, seven or more, or eight of the isolated biomarkers selected from the group consisting of lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

[0139] In another aspect, the kits can be used to detect one or more, two or more, three or more, four or more, five or more, six or more, seven or more, or eight of the isolated biomarkers selected from the group consisting of Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

[0140] The kit can include one or more agents for detection of biomarkers, a container for holding a biological sample isolated from a pregnant female; and printed instructions for reacting agents with the biological sample or a portion of the biological sample to detect the presence or amount of the isolated biomarkers in the biological sample. The agents can be packaged in separate containers. The kit can further comprise one or more control reference samples and reagents for performing an immunoassay.

[0141] In one embodiment, the kit comprises agents for measuring the levels of at least N of the isolated biomarkers listed in Tables 1 through 63. The kit can include antibodies that specifically bind to these biomarkers, for example, the kit can contain at least one of an antibody that specifically binds to lipopolysaccharide-binding protein (LBP), an antibody that specifically binds to prothrombin (THRB), an antibody that specifically binds to complement component C5 (C5 or CO5), an antibody that specifically binds to plasminogen (PLMN), and an antibody that specifically binds to complement component C8 gamma chain (C8G or CO8G).

[0142] In one embodiment, the kit comprises agents for measuring the levels of at least N of the isolated biomarkers listed in Tables 1 through 63. The kit can include antibodies that specifically bind to these biomarkers, for example, the kit can contain at least one of an antibody that specifically binds to Alpha-1B-glycoprotein (A1BG), Disintegrin and metalloproteinase domain-containing protein 12 (ADA12), Apolipoprotein B-100 (APOB), Beta-2-microglobulin (B2MG), CCAAT/enhancer-binding protein alpha/beta (HP8 Peptide), Corticosteroid-binding globulin (CBG), Complement component C6, Endoglin (EGLN), Ectonucleotide pyrophosphatase/phosphodiesterase family member 2 (ENPP2), Coagulation factor VII (FA7), Hyaluronan-binding protein 2 (HABP2), Pregnancy-specific beta-1-glycoprotein 9 (PSG9), Inhibin beta E chain (INHBE).

[0143] The kit can comprise one or more containers for compositions contained in the kit. Compositions can be in liquid form or can be lyophilized. Suitable containers for the compositions include, for example, bottles, vials, syringes, and test tubes. Containers can be formed from a variety of materials, including glass or plastic. The kit can also comprise a package insert containing written instructions for methods of determining probability of preterm birth.

[0144] From the foregoing description, it will be apparent that variations and modifications can be made to the invention described herein to adopt it to various usages and conditions. Such embodiments are also within the scope of the following claims.

[0145] The recitation of a listing of elements in any definition of a variable herein includes definitions of that variable as any single element or combination (or subcombination) of listed elements. The recitation of an embodiment herein includes that embodiment as any single embodiment or in combination with any other embodiments or portions thereof.

[0146] All patents and publications mentioned in this specification are herein incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference.

[0147] The following examples are provided by way of illustration, not limitation.

EXAMPLES

Example 1. Development of Sample Set for Discovery and Validation of Biomarkers for Preterm Birth

[0148] A standard protocol was developed governing conduct of the Proteomic Assessment of Preterm Risk (PAPR) clinical study. This protocol also specified that the samples and clinical information could be used to study other pregnancy complications for some of the subjects. Specimens were obtained from women at 11 Internal Review Board (IRB) approved sites across the United States. After providing informed consent, serum and plasma samples were obtained, as well as pertinent information regarding the patient's demographic characteristics, past medical and pregnancy history, current pregnancy history and concurrent medications. Following delivery, data were collected relating to maternal and infant conditions and complications. Serum and plasma samples were processed according to a protocol that requires standardized refrigerated centrifugation, aliquoting of the samples into 0.5 ml 2-D bar-coded cryovials and subsequent freezing at −80° C.

[0149] Following delivery, preterm birth cases were individually reviewed to determine their status as either a spontaneous preterm birth or a medically indicated preterm birth. Only spontaneous preterm birth cases were used for this analysis. For discovery of biomarkers of preterm birth, 80 samples were analyzed in two gestational age groups: a) a late window composed of samples from 23-28 weeks of gestation which included 13 cases, 13 term controls matched within one week of sample collection and 14 term random controls, and, b) an early window composed of samples from 17-22 weeks of gestation included 15 cases, 15 term controls matched within one week of sample collection and 10 random term controls.

[0150] The samples were subsequently depleted of high abundance proteins using the Human 14 Multiple Affinity Removal System (MARS 14), which removes 14 of the most abundant proteins that are treated as uninformative with regard to the identification for disease-relevant changes in the serum proteome. To this end, equal volumes of each clinical or a pooled human serum sample (HGS) sample were diluted with column buffer and filtered to remove precipitates. Filtered samples were depleted using a MARS-14 column (4.6×100 mm, Cat. #5188-6558, Agilent Technologies). Samples were chilled to 4° C. in the autosampler, the depletion column was run at room temperature, and collected fractions were kept at 4° C. until further analysis. The unbound fractions were collected for further analysis.

[0151] A second aliquot of each clinical serum sample and of each HGS was diluted into ammonium bicarbonate buffer and depleted of the 14 high and approximately 60 additional moderately abundant proteins using an IgY14-SuperMix (Sigma) hand-packed column, comprised of 10 mL of bulk material (50% slurry, Sigma). Shi et al., Methods, 56(2):246-53 (2012). Samples were chilled to 4° C. in the autosampler, the depletion column was run at room temperature, and collected fractions were kept at 4° C. until further analysis. The unbound fractions were collected for further analysis.

[0152] Depleted serum samples were denatured with trifluorethanol, reduced with dithiotreitol, alkylated using iodoacetamide, and then digested with trypsin at a 1:10 trypsin: protein ratio. Following trypsin digestion, samples were desalted on a C18 column, and the eluate lyophilized to dryness. The desalted samples were resolubilized in a reconstitution solution containing five internal standard peptides.

[0153] Depleted and trypsin digested samples were analyzed using a scheduled Multiple Reaction Monitoring method (sMRM). The peptides were separated on a 150 mm×0.32 mm Bio-Basic C18 column (ThermoFisher) at a flow rate of 5 μl/min using a Waters Nano Acquity UPLC and eluted using an acetonitrile gradient into a AB SCIEX QTRAP 5500 with a Turbo V source (AB SCIEX, Framingham, Mass.). The sMRM assay measured 1708 transitions that correspond to 854 peptides and 236 proteins. Chromatographic peaks were integrated using Rosetta Elucidator software (Ceiba Solutions).

[0154] Transitions were excluded from analysis, if their intensity area counts were less than 10000 and if they were missing in more than three samples per batch. Intensity area counts were log transformed and Mass Spectrometry run order trends and depletion batch effects were minimized using a regression analysis.

Example 2. Analysis I of Transitions to Identify Preterm Birth Biomarkers

[0155] The objective of these analyses was to examine the data collected in Example 1 to identify transitions and proteins that predict preterm birth. The specific analyses employed were (i) Cox time-to-event analyses and (ii) models with preterm birth as a binary categorical dependent variable. The dependent variable for all the Cox analyses was Gestational Age of time to event (where event is preterm birth). For the purpose of the Cox analyses, preterm birth subjects have the event on the day of birth. Term subjects are censored on the day of birth. Gestational age on the day of specimen collection is a covariate in all Cox analyses.

[0156] The assay data were previously adjusted for run order and depletion batch, and log transformed. Values for gestational age at time of sample collection were adjusted as follows. Transition values were regressed on gestational age at time of sample collection using only controls (non-pre-term subjects). The residuals from the regression were designated as adjusted values. The adjusted values were used in the models with pre-term birth as a binary categorical dependent variable. Unadjusted values were used in the Cox analyses.

[0157] Univariate Cox Proportional Hazards Analyses

[0158] Univariate Cox Proportional Hazards analyses was performed to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. Table 1 shows the transitions with p-values less than 0.05. Five proteins have multiple transitions among those with p-value less than 0.05: lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

[0159] Multivariate Cox Proportional Hazards Analyses: Stepwise AIC selection

[0160] Cox Proportional Hazards analyses was performed to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate, using stepwise and lasso models for variable selection. These analyses include a total of n=80 subjects, with number of PTB events=28. The stepwise variable selection analysis used the Akaike Information Criterion (AIC) as the stopping criterion. Table 2 shows the transitions selected by the stepwise AIC analysis. The coefficient of determination (R.sup.2) for the stepwise AIC model is 0.86 (not corrected for multiple comparisons).

[0161] Multivariate Cox Proportional Hazards Analyses: Lasso Selection

[0162] Lasso variable selection was used as the second method of multivariate Cox Proportional Hazards analyses to predict Gestational Age at Birth, including Gestational age on the day of specimen collection as a covariate. This analysis uses a lambda penalty for lasso estimated by cross validation. Table 3 shows the results. The lasso variable selection method is considerably more stringent than the stepwise AIC, and selects only 3 transitions for the final model, representing 3 different proteins. These 3 proteins give the top 4 transitions from the univariate analysis; 2 of the top 4 univariate are from the same protein, and hence are not both selected by the lasso method. Lasso tends to select a relatively small number of variables with low mutual correlation. The coefficient of determination (R.sup.2) for the lasso model is 0.21 (not corrected for multiple comparisons).

[0163] Univariate AUROC Analysis of Preterm Birth as a Binary Categorical Dependent Variable

[0164] Univariate analyses was performed to discriminate pre-term subjects from non-pre-term subjects (pre-term as a binary categorical variable) as estimated by area under the receiver operating characteristic (AUROC) curve. These analyses use transition values adjusted for gestational age at time of sample collection, as described above. Table 4 shows the AUROC curve for the 77 transitions with the highest AUROC area of 0.6 or greater.

[0165] Multivariate Analysis of Preterm Birth as a Binary Categorical Dependent Variable

[0166] Multivariate analyses was performed to predict preterm birth as a binary categorical dependent variable, using random forest, boosting, lasso, and logistic regression models. Random forest and boosting models grow many classification trees. The trees vote on the assignment of each subject to one of the possible classes. The forest chooses the class with the most votes over all the trees.

[0167] For each of the four methods (random forest, boosting, lasso, and logistic regression) each method was allowed to select and rank its own best 15 transitions. We then built models with 1 to 15 transitions. Each method sequentially reduces the number of nodes from 15 to 1 independently. A recursive option was used to reduce the number of nodes at each step: To determine which node to remove, the nodes were ranked at each step based on their importance from a nested cross-validation procedure. The least important node was eliminated. The importance measures for lasso and logistic regression are z-values. For random forest and boosting, the variable importance was calculated from permuting out-of-bag data: for each tree, the classification error rate on the out-of-bag portion of the data was recorded; the error rate was then recalculated after permuting the values of each variable (i.e., transition); if the transition was in fact important, there would have been be a big difference between the two error rates; the difference between the two error rates were then averaged over all trees, and normalized by the standard deviation of the differences. The AUCs for these models are shown in Table 5, as estimated by 100 rounds of bootstrap resampling. Table 6 shows the top 15 transitions selected by each multivariate method, ranked by importance for that method. These multivariate analyses suggest that models that combine 3 or more transitions give AUC greater than 0.7, as estimated by bootstrap.

[0168] In multivariate models, random forest (rf), boosting, and lasso models gave the best area under the AUROC curve. The following transitions were selected by these models, as significant in Cox univariate models, and/or having high univariate ROC'S:

TABLE-US-00007 AFTECCVVASQLR770.87_574.3 ELLESYIDGR_597.8_710.3 ITLPDFTGDLR_624.34920.4 TDAPDLPEENQAR_728.34613.3 SFRPFVPR_335.86_635.3

[0169] In summary, univariate and multivariate Cox analyses was performed using transitions to predict Gestational Age at Birth (GAB), including Gestational age on the day of specimen collection as a covariate. In the univariate Cox analysis, five proteins were identified that have multiple transitions among those with p-value less than 0.05: lipopolysaccharide-binding protein (LBP), prothrombin (THRB), complement component C5 (C5 or CO5), plasminogen (PLMN), and complement component C8 gamma chain (C8G or CO8G).

[0170] In multivariate Cox analyses, stepwise AIC variable analysis selects 24 transitions, while the lasso model selects 3 transitions, which include the 3 top proteins in the univariate analysis. Univariate (AUROC) and multivariate (random forest, boosting, lasso, and logistic regression) analyses were performed to predict pre-term birth as a binary categorical variable. Univariate analyses identified 63 analytes with AUROC of 0.6 or greater. Multivariate analyses suggest that models that combine 3 or more transitions give AUC greater than 0.7, as estimated by bootstrap.

Example 3. Study II to Identify and Confirm Preterm Birth Biomarkers

[0171] A further study was performed using essentially the same methods described in the preceding Examples unless noted below. In this study, 2 gestational aged matched controls were used for each case of 28 cases and 56 matched controls, all from the early gestational window only (17-22 weeks).

[0172] The samples were processed in 4 batches with each batch composed of 7 cases, 14 matched controls and 3 HGS controls. Serum samples were depleted of the 14 most abundant serum samples by MARS14 as described in Example 1. Depleted serum was then reduced with dithiothreitol, alkylated with iodacetamide, and then digested with trypsin at a 1:20 trypsin to protein ratio overnight at 37° C. Following trypsin digestion, the samples were desalted on an Empore C18 96-well Solid Phase Extraction Plate (3M Company) and lyophilized to dryness. The desalted samples were resolubilized in a reconstitution solution containing five internal standard peptides.

[0173] The LC-MS/MS analysis was performed with an Agilent Poroshell 120 EC-C18 column (2.1×50 mm, 2.7 μm) and eluted with an acetonitrile gradient into a Agilent 6490 Triple Quadrapole mass spectrometer.

[0174] Data analysis included the use of conditional logistic regression where each matching triplet (case and 2 matched controls) was a stratum. The p-value reported in the table indicates whether there is a significant difference between cases and matched controls.

TABLE-US-00008 TABLE 7 Results of Study II Transi- tion Protein Annotation p-value DFHIN CFAB_ Complement 0.006729512 LFQVL HUMAN factor B PWLK ITLPD LBP_ Lipopolysaccharide- 0.012907017 FTGDL HUMAN binding R protein WWGGQ ENPP2_ Ectonucleotide 0.013346 PLWIT HUMAN pyrophosphatase/ ATK Phosphodiesterase family member 2 TASDF GELS_ Gelsolin 0.013841221 ITK HUMAN AGLLR PGRP2_ N-acetylmuramoyl- 0.014241979 PDYAL HUMAN L-alanine LGHR amidase FLQEQ CO8G_ Complement 0.014339596 GHR HUMAN component C8 gamma chain FLNWI HABP2_ Hyaluronan-binding 0.014790418 K HUMAN protein 2 EKPAG BPIB1_ BPI fold- 0.019027746 GIPVL HUMAN containing GSLVN family B TVLK member 1 ITGFL LBP_ Lipopolysaccharide- 0.019836986 KPGK HUMAN binding protein YGLVT CFAB_ Complement 0.019927774 YATYP HUMAN factor B K SLLQP CO8A_ Complement 0.020930939 NK HUMAN component C8 alpha chain DISEV CFAB_ Complement 0.021738046 VTPR HUMAN factor B VQEAH CO8G_ Complement 0.021924548 LTEDQ HUMAN component C8 IFYFP gamma chain K SPELQ APOA2_ Apolipoprotein 0.025944285 AEAK HUMAN A-II TYLHT ENPP2_ Ectonucleotide 0.026150038 YESEI HUMAN pyrophosphatase/ phosphodiesterase family member 2 DSPSV PROF1_ Profilin-1 0.026607371 WAAVP HUMAN GK HYINL NPY_ Pro-neuropeptide 0.027432804 ITR HUMAN Y SLPVS CO8G_ Complement 0.029647857 DSVLS HUMAN component C8 GFEQR gamma chain IPGIF CO8B_ Complement 0.030430996 ELGIS HUMAN component SQSDR C8 beta chain IQTHS F13B_ Coagulation 0.031667664 TTYR HUMAN factor XIII B chain DGSPD PGRP2_ N-acetylmuramoyl- 0.034738338 VTTAD HUMAN L-alanine amidase IGANT PDATK QLGLP ITIH4_ Inter-alpha- 0.043130591 GPPDV HUMAN trypsin PDHAA inhibitor YHPF heavy chain H4 FPLGS LCAP_ Leucyl-cystinyl 0.044698045 YTIQN HUMAN aminopeptidase IVAGS TYLFS TK AHYDL FETUA_ Alpha-2-HS- 0.046259201 R HUMAN glycoprotein SFRPF LBP_ Lipopolysaccharide- 0.047948847 VPR HUMAN binding protein

Example 4. Study III Shotgun Identification of Preterm Birth Biomarkers

[0175] A further study used a hypothesis-independent shotgun approach to identify and quantify additional biomarkers not present on our multiplexed hypothesis dependent MRM assay. Samples were processed as described in the preceding Examples unless noted below.

[0176] Tryptic digests of MARS depleted patient (preterm birth cases and term controls) samples were fractionated by two-dimensional liquid chromatography and analyzed by tandem mass spectrometry. Aliquots of the samples, equivalent to 3-4 μl of serum, were injected onto a 6 cm×75 μm self-packed strong cation exchange (Luna SCX, Phenomenex) column. Peptides were eluded from the SCX column with salt (15, 30, 50, 70, and 100% B, where B=250 mM ammonium acetate, 2% acetonitrile, 0.1% formic acid in water) and consecutively for each salt elution, were bound to a 0.5 μl C18 packed stem trap (Optimize Technologies, Inc.) and further fractionated on a 10 cm×75 μm reversed phase ProteoPep II PicoFrit column (New Objective). Peptides were eluted from the reversed phase column with an acetonitrile gradient containing 0.1% formic acid and directly ionized on an LTQ-Orbitrap (ThermoFisher). For each scan, peptide parent ion masses were obtained in the Orbitrap at 60K resolution and the top seven most abundant ions were fragmented in the LTQ to obtain peptide sequence information.

[0177] Parent and fragment ion data were used to search the Human RefSeq database using the Sequest (Eng et al., J. Am. Soc. Mass Spectrom 1994; 5:976-989) and X! Tandem (Craig and Beavis, Bioinformatics 2004; 20:1466-1467) algorithms. For Sequest, data was searched with a 20 ppm tolerance for the parent ion and 1 AMU for the fragment ion. Two missed trypsin cleavages were allowed, and modifications included static cysteine carboxyamidomethylation and methionine oxidation. After searching the data was filtered by charge state vs. Xcorr scores (charge+1≥1.5 Xcorr, charge+2≥2.0, charge+3≥2.5). Similar search parameters were used for X!tandem, except the mass tolerance for the fragment ion was 0.8 AMU and there is no Xcorr filtering. Instead, the PeptideProphet algorithm (Keller et al., Anal. Chem 2002; 74:5383-5392) was used to validate each X!Tandem peptide-spectrum assignment and Protein assignments were validated using ProteinProphet algorithm (Nesvizhskii et al., Anal. Chem 2002; 74:5383-5392). Data was filtered to include only the peptide-spectrum matches that had PeptideProphet probability of 0.9 or more. After compiling peptide and protein identifications, spectral count data for each peptide were imported into DAnTE software (Polpitiya et al., Bioinformatics. 2008; 24:1556-1558). Log transformed data was mean centered and missing values were filtered, by requiring that a peptide had to be identified in at least 4 cases and 4 controls. To determine the significance of an analyte, Receiver Operating Characteristic (ROC) curves for each analyte were created where the true positive rate (Sensitivity) is plotted as a function of the false positive rate (1-Specificity) for different thresholds that separate the SPTB and Term groups. The area under the ROC curve (AUC) is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. Peptides with AUC greater than or equal to 0.6 found uniquely by Sequest or Xtandem are found in Tables 8 and 9, respectively, and those identified by both approaches are found in Table 10.

TABLE-US-00009 TABLE 8 Significant peptides (AUC > 0.6) for Sequest only Uniprot ID S_ Protein Description (name) Peptide AUC 5'-AMP-activated Q9UGI9 K.LVIFDTM*L 0.78 protein kinase (AAKG3_ EIK.K subunit gamma-3 HUMAN) afamin precursor P43652 K.FIEDNIEYIT 0.79 (AFAM_ IIAFAQYVQEAT HUMAN) FEEMEK.L afamin precursor P43652 K.IAPQLSTEEL 0.71 (AFAM_ VSLGEK.M HUMAN) afamin precursor P43652 K.LKHELTDEE 0.60 (AFAM_ LQSLFTNFANV HUMAN) VDK.C afamin precursor P43652 K.LPNNVLQEK.I 0.60 (AFAM_ HUMAN) afamin precursor P43652 K.SDVGFLPPFPT 0.71 (AFAM_ LDPEEK.C HUMAN) afamin precursor P43652 K.VMNHICSK.Q 0.68 (AFAM_ HUMAN) afamin precursor P43652 R.ESLLNHFLY 0.69 (AFAM_ EVAR.R HUMAN) afamin precursor P43652 R.LCFFYNKK.S 0.69 (AFAM_ HUMAN) alpha-1- P01011 K.AVLDVFEEGT 0.72 antichymotrypsin (AACT_ EASAATAVK.I HUMAN) alpha-1- P01011 K.EQLSLLDR.F 0.65 antichymotrypsin (AACT_ precursor HUMAN) alpha-1- P01011 K.EQLSLLDRFTE 0.64 antichymotrypsin (AACT_ DAK.R precursor HUMAN) alpha-1- P01011 K.EQLSLLDRFT 0.60 antichymotrypsin (AACT_ EDAKR.L precursor HUMAN) alpha-1- P01011 K.ITDLIKDLDSQT 0.65 antichymotrypsin (AACT_ MM*VLVNYIFFK.A precursor HUMAN) alpha-1- P01011 K.ITLLSALVET 0.62 antichymotrypsin (AACT_ R.T precursor HUMAN) alpha-1- P01011 K.RLYGSEAFATDF 0.62 antichymotrypsin (AACT_ QDSAAAK.K precursor HUMAN) alpha-1- P01011 R.EIGELYLPK.F 0.65 antichymotrypsin (AACT_ precursor HUMAN) alpha-lB- P04217 R.CEGPIPDVTFE 0.67 glycoprotein (A1BG_ LLR.E precursor HUMAN) alpha-lB- P04217 R.FALVR.E 0.79 glycoprotein (A1BG_ precursor HUMAN) alpha-2- P08697 K.SPPGVCSR.D 0.81 antiplasmin (A2AP_ isoform a HUMAN) precursor alpha-2- P08697 R.DSFHLDEQFTV 0.69 antiplasmin (A2AP_ PVEMMQAR.T isoform a HUMAN) precursor alpha-2-HS- P02765 K.CNLLAEK.Q 0.67 glycoprotein (FETUA_ preproprotein HUMAN) alpha-2-HS- P02765 K.EHAVEGDCDFQ 0.67 glycoprotein (FETUA_ LLK.L preproprotein HUMAN) alpha-2-HS- P02765 K.HTLNQIDEVKV 0.64 glycoprotein (FETUA_ WPQQPSGELFEIE preproprotein HUMAN) IDTLETTCHVLDP TPVAR.C alpha-2- P01023 K.MVSGFIPLK 0.73 macroglobulin (A2MG_ PTVK.M precursor HUMAN) alpha-2- P01023 R.AFQPFFVELT 0.68 macroglobulin (A2MG_ M*PYSVIR.G precursor HUMAN) alpha-2- P01023 R.AFQPFFVEL 0.62 macroglobulin (A2MG_ TMPYSVIR.G precursor HUMAN) alpha-2- P01023 R.NQGNTWLTA 0.73 macroglobulin (A2MG_ FVLK.T precursor HUMAN) angiotensinogen P01019 K.IDRFMQAVT 0.81 preproprotein (ANGT_ GWK.T HUMAN) angiotensinogen P01019 K.LDTEDKLR.A 0.72 preproprotein (ANGT_ HUMAN) angiotensinogen P01019 K.TGCSLMGASV 0.64 preproprotein (ANGT_ DSTLAF HUMAN) NTYVHFQGK.M angiotensinogen P01019 R.AAMVGMLANF 0.62 preproprotein (ANGT_ LGFR.I HUMAN) antithrombin-III P01008 K.NDNDNIFLS 0.64 precursor (ANT3_ PLSIST HUMAN) AFAMTK.L antithrombin-III P01008 K.SKLPGIVA 0.81 precursor (ANT3_ EGRDDLY HUMAN) VSDAFHK.A antithrombin-III P01008 R.EVPLNTIIF 0.61 precursor (ANT3_ MGR.V HUMAN) antithrombin-III P01008 R.FATTFYQHL 0.66 precursor (ANT3_ ADSKNDNDNIF HUMAN) LSPLSISTAFA MTK.L antithrombin-III P01008 R.ITDVIPSE 0.60 precursor (ANT3_ AINELTVL HUMAN) VLVNTIYFK.G antithrombin-III P01008 R.RVWELSK.A 0.63 precursor (ANT3_ HUMAN) antithrombin-III P01008 R.VAEGTQVLELP 0.62 precursor (ANT3_ FKGDDITM*VLIL HUMAN) PKPEK.S antithrombin-III P01008 R.VAEGTQVLELP 0.62 precursor (ANT3_ FKGDDITMVLILP HUMAN) KPEK.S apolipoprotein A-II P02652 K.AGTELVNFLSY 0.61 preproprotein (APOA2_ FVELGTQPATQ.- HUMAN) apolipoprotein A-II P02652 K.EPCVESLVSQY 0.63 preproprotein (APOA2_ FQTVTDYGK.D HUMAN) apolipoprotein A-IV P06727 K. ALVQQMEQLR.Q 0.61 precursor (APOA4_ HUMAN) apolipoprotein A-IV P06727 K.LGPHAGDVEGH 0.61 precursor (APOA4_ LSFLEK.D HUMAN) apolipoprotein A-IV P06727 K.SELTQQLNAL 0.71 precursor (APOA4_ FQDK.L HUMAN) apolipoprotein A-IV P06727 K.SLAELGGHLD 0.61 precursor (APOA4_ QQVEEFRR.R HUMAN) apolipoprotein A-IV P06727 K.VKIDQTVEEL 0.75 precursor (APOA4_ RR.S HUMAN) apolipoprotein A-IV P06727 K.VNSFFSTFK.E 0.63 precursor (APOA4_ HUMAN) apolipoprotein P04114 K.ATFQTPDFIVP 0.65 B-100 (APOB_ LTDLR.I precursor HUMAN) apolipoprotein P04114 K.AVSM*PSFSIL 0.65 B-100 (APOB_ GSDVR.V precursor HUMAN) apolipoprotein P04114 K.AVSMPSFSILG 0.67 B-100 (APOB_ SDVR.V precursor HUMAN) apolipoprotein P04114 K.EQHLFLPFSY 0.65 B-100 (APOB_ K.N precursor HUMAN) apolipoprotein P04114 K.KIISDYHQQF 0.63 B-100 (APOB_ R.Y precursor HUMAN) apolipoprotein P04114 K.QVFLYPEKDEPT 0.64 B-100 (APOB_ YILNIK.R precursor HUMAN) apolipoprotein P04114 K.SPAFTDLHLR.Y 0.69 B-100 (APOB_ precursor HUMAN) apolipoprotein P04114 K.TILGTMPAFEVS 0.62 B-100 (APOB_ LQALQK.A precursor HUMAN) apolipoprotein P04114 K.VLADKFIIPGL 0.72 B-100 (APOB_ K.L precursor HUMAN) apolipoprotein P04114 K.YSQPEDSLIPFF 0.61 B-100 (APOB_ EITVPESQLTVSQF precursor HUMAN) TLPK.S apolipoprotein P04114 R.DLKVEDIPLA 0.64 B-100 (APOB_ R.I precursor HUMAN) apolipoprotein P04114 R.GIISALLVPPE 0.81 B-100 (APOB_ TEEAK.Q precursor HUMAN) apolipoprotein P04114 R.ILGEELGFASL 0.62 B-100 (APOB_ HDLQLLGK.L precursor HUMAN) apolipoprotein P04114 R.LELELRPTGEI 0.60 B-100 (APOB_ EQYSVSATYELQ precursor HUMAN) R.E apolipoprotein P04114 R.NIQEYLSILT 0.68 B-100 (APOB_ DPDGK.G precursor HUMAN) apolipoprotein P04114 R.TFQIPGYTVPV 0.75 B-100 (APOB_ VNVEVSPFTIEMS precursor HUMAN) AFGYVFPK.A apolipoprotein P04114 R.TIDQMLNSELQ 0.70 B-100 (APOB_ WPVPDIYLR.D precursor HUMAN) apolipoprotein P02654 K.MREWFSETFQ 0.61 C-I (APOC1_ K.V precursor HUMAN) apolipoprotein P02655 K.STAAMSTYTGI 0.61 C-II (APOC2_ FTDQVLSVLKGE precursor HUMAN) E.- apolipoprotein P02656 R.GWVTDGFSSL 0.62 C-III (APOC3_ K.D precursor HUMAN) apolipoprotein P02649 R.AATVGSLAGQP 0.61 E (APOE_ LQER.A precursor HUMAN) apolipoprotein P02649 R.LKSWFEPLVED 0.65 E (APOE_ MQR.Q precursor HUMAN) apolipoprotein P02649 R.WVQTLSEQVQE 0.64 E (APOE_ ELLSSQVTQELR.A precursor HUMAN) ATP-binding O14678 K.LCGGGRWELM* 0.60 cassette (ABCD4_ R.I sub-family HUMAN) D member 4 ATP-binding Q9NUQ8 K.LPGLLK.R 0.73 cassette (ABCF3_ sub-family HUMAN) F member 3 beta-2- P02749 K.EHSSLAFWK.T 0.64 glycoprotein 1 (APOH_ precursor HUMAN) beta-2- P02749 R.TCPKPDDLPFS 0.60 glycoprotein (APOH_ TVVPLK.T 1 HUMAN) precursor beta-2- P02749 R.VCPFAGILENG 0.68 glycoprotein (APOH_ AVR.Y 1 HUMAN) precursor beta-Ala-Flis Q96KN2 K.LFAAFFLEMAQ 0.68 dipeptidase (CNDP1_ LH.- precursor HUMAN) biotinidase P43251 K.SHLIIAQVAK. 0.62 precursor (BTD_ N HUMAN) carboxypeptidase Q96IY4 K.NAIWIDCGIHA 0.62 B2 (CBPB2_ R.E preproprotein HUMAN) carboxypeptidase P15169 R.EALIQFLEQVH 0.69 N (CBPN_ QGIK.G catalytic HUMAN) chain precursor carboxypeptidase N P22792 R.LLNIQTYCAGP 0.62 subunit 2 (CPN2_ AYLK.G precursor HUMAN) catalase P04040 R.LCENIAGHLKD 0.62 (CATA_ AQIFIQK.K HUMAN) ceruloplasmin P00450 K.AETGDKVYVHL 0.61 precursor (CERU_ K.N HUMAN) ceruloplasmin P00450 K.AGLQAFFQVQE 0.62 precursor (CERU_ CNK.S HUMAN) ceruloplasmin P00450 K.DIASGLIGPLI 0.63 precursor (CERU_ ICK.K HUMAN) ceruloplasmin P00450 K.DIFTGLIGPM* 0.63 precursor (CERU_ K.I HUMAN) ceruloplasmin P00450 K.DIFTGLIGPM 0.68 precursor (CERU_ K.I HUMAN) ceruloplasmin P00450 K.M*YYSAVDPTK 0.62 precursor (CERU_ DIFTGLIGPMK.I HUMAN) ceruloplasmin P00450 K.MYYSAVDPTKD 0.63 precursor (CERU_ IFTGLIGPM*K.I HUMAN) ceruloplasmin P00450 K.PVWLGFLGPII 0.63 precursor (CERU_ K.A HUMAN) ceruloplasmin P00450 R.ADDKVYPGEQY 0.64 precursor (CERU_ TYMLLATEEQSPG HUMAN) EGDGNCVTR.I ceruloplasmin P00450 R.DTANLFPQTSL 0.71 precursor (CERU_ TLHM*WPDTEGTF HUMAN) NVECLTTDHYTGG MK.Q ceruloplasmin P00450 R.DTANLFPQTSL 0.68 precursor (CERU_ TLHMWPDTEGTFN HUMAN) VECLTTDHYTGGM K.Q ceruloplasmin P00450 R.FNKNNEGTYYS 0.74 precursor (CERU_ PNYNPQSR.S HUMAN) ceruloplasmin P00450 R.IDTINLFPATL 0.75 precursor (CERU_ FDAYM*VAQNPGE HUMAN) WM*LSCQNLNHLK .A ceruloplasmin P00450 R.IDTINLFPATL 0.86 precursor (CERU_ FDAYM*VAQNPGE HUMAN) WMLSCQNLNHLK. A ceruloplasmin P00450 R.IDTINLFPATL 0.60 precursor (CERU_ FDAYMVAQNPGEW HUMAN) M*LSCQNLNHLK. A ceruloplasmin P00450 R.KAEEEHLGILG 0.71 precursor (CERU_ PQLHADVGDKVK. HUMAN) I ceruloplasmin P00450 R.TTIEKPVWLGF 0.63 precursor (CERU_ LGPIIK.A HUMAN) cholinesterase P06276 R.FWTSFFPK.V 0.76 precursor (CHLE_ HUMAN) clusterin P10909 K.LFDSDPITVTV 0.78 preproprotein (CLUS_ PVEVSR.K HUMAN) clusterin P10909 R.ASSIIDELFQD 0.68 preproprotein (CLUS_ R.F HUMAN) coagulation P00740 K.WIVTAAHCVET 0.60 factor (FA9_ GVK.I IX HUMAN) preproprotein coagulation P08709 R.FSLVSGWGQLL 0.78 factor (FA7_ DR.G VII HUMAN) isoform a preproprotein coagulation P00742 K.ETYDFDIAVLR 0.75 factor (FA10_ .L X HUMAN) preproprotein coiled-coil Q8IYE1 K.VRQLEMEIGQ. 0.67 domain- (CCD13_ LNVHYLR.N containing HUMAN) protein 13 complement P02745 R.PAFSAIR.R 0.66 C1q (C1QA_ subcomponent HUMAN) subunit A precursor complement P02746 K.VVTFCDYAYNT 0.63 C1q (C1QB_ FQVTTGGMVLK.L subcomponent HUMAN) subunit B precursor complement P02747 K.FQSVFTVTR.Q 0.63 C1q (C1QC_ subcomponent HUMAN) subunit C precursor complement P00736 K.TLDEFTIIQNL 0.62 C1r (C1R_ QPQYQFR.D subcomponent HUMAN) precursor complement P00736 R.MDVFSQNMFCA 0.68 C1r (C1R_ GHPSLK.Q subcomponent HUMAN) precursor complement P00736 R.WILTAAHTLYP 0.74 C1r (C1R_ K.E subcomponent HUMAN) precursor complement C1s P09871 K.FYAAGLVSWGP 0.68 subcomponent (C1S_ Q.CGTYGLYTR.V precursor HUMAN) complement C1s P09871 K.GFQVVVTLR.R 0.63 subcomponent (C1S_ precursor HUMAN) complement C2 P06681 R.GALISDQWVLT 0.61 isoform 3 (CO2_ AAHCFR.D HUMAN) complement C2 P06681 R.PICLPCTMEAN 0.66 isoform 3 (CO2_ LALR.R HUMAN) complement C3 P01024 R.YYGGGYGSTQA 0.75 precursor (CO3_ TFMVFQALAQYQK HUMAN) .D complement C4-A P0COL4 K.GLCVATPVQLR 0.74 isoform 1 (CO4A_ .V HUMAN) complement C4-A P0COL4 K.M*RPSTDTITV 0.83 isoform 1 (CO4A_ M*VENSHGLR.V HUMAN) complement C4-A P0COL4 K.MRPSTDTITVM 0.72 isoform 1 (CO4A_ *VENSHGLR.V HUMAN) complement C4-A P0COL4 K.VGLSGM*AIAD 0.71 isoform 1 (CO4A_ VTLLSGFHALR.A HUMAN) complement C4-A P0COL4 K.VLSLAQEQVGG 0.63 isoform 1 (CO4A_ SPEK.L HUMAN) complement C4-A P0COL4 R.EMSGSPASGIP 0.65 isoform 1 (CO4A_ VK.V HUMAN) complement C4-A P0COL4 R.GCGEQTM*IYL 0.75 isoform 1 (CO4A_ APTLAASR.Y HUMAN) complement C4-A P0COL4 R.GLQDEDGYR.M 0.75 isoform 1 (CO4A_ HUMAN) complement C4-A P0COL4 R.GQIVFMNREP 0.93 isoform 1 (CO4A_ K.R HUMAN) complement C4-A P0COL4 R.KKEVYM*PSSI 0.72 isoform 1 (CO4A_ FQDDFVIPDISEP HUMAN) GTWK.I complement C4-A P0COL4 R.LPMSVR.R 0.78 isoform 1 (CO4A_ HUMAN) complement C4-A P0COL4 R.LTVAAPPSGGP 0.84 isoform 1 (CO4A_ GFLSIER.P HUMAN) complement C4-A P0COL4 R.NFLVR.A 0.75 isoform 1 (CO4A_ HUMAN) complement C4-A P0COL4 R.NGESVKLHLET 0.88 isoform 1 (CO4A_ DSLALVALGALDT HUMAN) ALYAAGSK.S complement C4-A P0COL4 R.QGSFQ.GGFR. 0.60 isoform 1 (CO4A_ S HUMAN) complement C4-A P0COL4 R.TLEIPGNSDPN 0.69 isoform 1 (CO4A_ MIPDGDFNSYVR. HUMAN) V complement C4-A P0COL4 R.VTASDPLDTLG 0.63 isoform 1 (CO4A_ SEGALSPGGVASL HUMAN) LR.L complement C4-A P0COL4 R.YLDKTEQWSTL 0.67 isoform 1 (CO4A_ PPETK.D HUMAN) complement C5 P01031 K.ADNFLLENTLP 0.63 preproprotein (CO5_ AQSTFTLAISAYA HUMAN) LSLGDK.T complement C5 P01031 K.ALVEGVDQLFT 0.63 preproprotein (CO5_ DYQIK.D HUMAN) complement C5 P01031 K.DGHVILQLNSI 0.62 preproprotein (CO5_ PSSDFLCVR.F HUMAN) complement C5 P01031 K.DVFLEMNIPYS 0.63 preproprotein (CO5_ VVR.G HUMAN) complement C5 P01031 K.EFPYRIPLDLV 0.60 preproprotein (CO5_ PK.T HUMAN) complement C5 P01031 K.FQNSAILTIQP 0.67 preproprotein (CO5_ K.Q HUMAN) complement C5 P01031 K.VFKDVFLEMNI 0.63 preproprotein (CO5_ PYSVVR.G HUMAN) complement C5 P01031 R.VFQFLEK.S 0.61 preproprotein (CO5_ HUMAN) complement P13671 K.DLHLSDVFLK. 0.60 component C6 (CO6_ A precursor HUMAN) complement P13671 R.TECIKPVVQEV 0.62 component C6 (CO6_ LTITPFQR.L precursor HUMAN) complement P10643 K.SSGWHFVVK.F 0.61 component C7 (CO7_ precursor HUMAN) complement P10643 R.ILPLTVCK.M 0.75 component C7 (CO7_ precursor HUMAN) complement P07357 R.ALDQYLMEFNA 0.65 component (CO8A_ CR.C C8 alpha HUMAN) chain precursor complement P07360 K.YGFCEAADQFH 0.60 component C8 (CO8G_ VLDEVR.R gamma chain HUMAN) precursor complement P02748 R.AIEDYINEFSV 0.69 component C9 (CO9_ RK.C precursor HUMAN) complement P02748 R.TAGYGINILGM 0.69 component C9 (CO9_ DPLSTPFDNEFYN precursor HUMAN) GLCNR.D complement P00751 K.ALFVSEEEKK. 0.64 factor B (CFAB_ L preproprotein HUMAN) complement P00751 K.CLVNLIEK.V 0.70 factor B (CFAB_ HUMAN) preproprotein complement P00751 K.EAGIPEFYDYD 0.66 factor B (CFAB_ VALIK.L preproprotein HUMAN) complement P00751 K.VSEADSSNADW 0.73 factor B (CFAB_ VTK.Q preproprotein HUMAN) complement P00751 K.YGQTIRPICLP 0.67 factor B (CFAB_ CTEGTTR.A preproprotein HUMAN) complement P00751 R.DLEIEVVLFHP 0.71 factor B (CFAB_ NYNINGK.K preproprotein HUMAN) complement P00751 R.FLCTGGVSPYA 0.64 factor B (CFAB_ DPNTCR.G preproprotein HUMAN) complement P08603 K.DGWSAQPTCI 0.80 factor H (CFAH_ K.S isoform a HUMAN) precursor complement P08603 K.EGWIHTVCING 0.67 factor H (CFAH_ R.W isoform a HUMAN) precursor complement P08603 K.TDCLSLPSFEN 0.61 factor H (CFAH_ AIPMGEK.K isoform a HUMAN) precursor complement P08603 R.DTSCVNPPTVQ 0.60 factor H (CFAH_ NAYIVSR.Q isoform a HUMAN) precursor complement P08603 K.CTSTGWIPAP 0.68 factor H (CFAH_ R.C isoform b HUMAN) precursor complement P08603 K.IIYKENER.F 0.76 factor H (CFAH_ isoform b HUMAN) precursor complement P08603 K.IVSSAM*EPDR 0.75 factor H (CFAH_ EYHFGQAVR.F isoform b HUMAN) precursor complement P08603 K.IVSSAMEPDRE 0.68 factor H (CFAH_ YHFGQAVR.F isoform b HUMAN) precursor complement P08603 R.CTLKPCDYPDI 0.81 factor H (CFAH_ K.H isoform b HUMAN) precursor complement P08603 R.KGEWVALNPL 0.60 factor H (CFAH_ R.K isoform b HUMAN) precursor complement P08603 R.KGEWVALNPLR 0.69 factor H (CFAH_ K.C isoform b HUMAN) precursor complement P08603 R.RPYFPVAVGK. 0.68 factor H (CFAH_ Y isoform b HUMAN) precursor complement Q03591 R.EIMENYNIAL 0.64 factor (FHR1_ R.W H-related HUMAN) protein 1 precursor complement P05156 K.DASGITCGGIY 0.71 factor 1 (CFAI_ IGGCWILTAAHCL preproprotein HUMAN) R.A complement P05156 K.VANYFDWISYH 0.72 factor 1 (CFAI_ VGR.P preproprotein HUMAN) complement P05156 R.IIFHENYNAGT 0.63 factor 1 (CFAI_ YQNDIALIEMK.K preproprotein HUMAN) complement P05156 R.YQIWTTVVDWI 0.63 factor 1 (CFAI_ HPDLK.R preproprotein HUMAN) conserved Q9Y2V7 K.ISNLLK.F 0.65 oligomeric (COG6_ Golgi complex HUMAN) subunit 6 isoform corticosteroid- P08185 R.WSAGLTSSQVD 0.62 binding (CBG_ LYIPK.V globulin HUMAN) precursor C-reactive P02741 K.YEVQGEVFTKP 0.60 protein (CRP_ QLWP.- precursor HUMAN) dopamine P09172 R.HVLAAWALG 0.88 beta- (DOPO_ AK.A hydroxylase HUMAN) precursor double- Q9INS39 R.AGLRYVCLAEP 0.75 stranded (RED2_ AER.R RNA-specific HUMAN) editase B2 dual Q9NRD8 R.FTQLCVKGGGG 0.65 oxidase 2 (DUOX2_ GGNGIR.D precursor HUMAN) FERM domain- Q9BZ67 R.VQLGPYQPGRP 0.65 containing (FRMD8_ AACDLR.E protein 8 HUMAN) fetuin-B Q9UGM5 R.GGLGSLFYLTL 0.83 precursor (FETUB_ DVLETDCHVLR.K HUMAN) ficolin-3 075636 R.ELLSQGATLSG 0.69 isoform 1 (FCN3_ WYHLCLPEGR.A precursor HUMAN) gastric P27352 K.KTTDM*ILNEI 0.60 intrinsic (IF_ KQGK.F factor HUMAN) precursor gelsolin P06396 K.NWRDPDQTDGL 0.72 isoform d (GELS_ GLSYLSSHIANVE HUMAN) R.V gelsolin P06396 K.TPSAAYLWVGT 0.80 isoform d (GELS_ GASEAEK.T HUMAN) gelsolin P06396 R.VEKFDLVPVPT 0.60 isoform d (GELS_ NLYGDFFTGDAYV HUMAN) ILK.T gelsolin P06396 R.VPFDAATLHT 0.67 isoform d (GELS_ STAM AAQHGM D HUMAN) DDGTGQK.Q glutathione P22352 K.FYTFLK.N 0.63 peroxidase 3 (GPX3_ precursor HUMAN) hemopexin P02790 K.GDKVWVYPPEK 0.65 precursor (HEMO_ K.E HUMAN) hemopexin P02790 K.LLQDEFPGIPS 0.71 precursor (HEMO_ PLDAAVECHR.G HUMAN) hemopexin P02790 K.SGAQATWTELP 0.64 precursor (HEMO_ WPHEK.V HUMAN) hemopexin P02790 K.SGAQATWTELP 0.61 precursor (HEMO_ WPHEKVDGALCME HUMAN) K.S hemopexin P02790 K.VDGALCMEK.S 0.66 precursor (HEMO_ HUMAN) hemopexin P02790 R.DYFMPCPGR.G 0.68 precursor (HEMO_ HUMAN) hemopexin P02790 R.EWFWDLATGTM 0.64 precursor (HEMO_ *K.E HUMAN) hemopexin P02790 R.QGHNSVFLIK. 0.71 precursor (HEMO_ G HUMAN) heparin P05546 K.HQGTITVN E 0.60 cofactor 2 (HEP2_ EGTQATTVTTVG precursor HUMAN) FMPLSTQVR.F heparin P05546 K.YEITTIHNLF 0.62 cofactor 2 (HEP2_ R.K precursor HUMAN) heparin cofactor 2 P05546 R.LNILNAK.F 0.68 precursor (HEP2_ HUMAN) heparin cofactor 2 P05546 R.NFGYTLR.S 0.64 precursor (HEP2_ HUMAN) heparin cofactor 2 P05546 R.VLKDQVNTFDN 0.63 precursor (HEP2_ IFIAPVGISTAMG HUMAN) M*ISLGLK.G hepatocyte cell Q14CZ8 K.PLLNDSRMLLS 0.61 adhesion molecule (HECAM_ PDQK.V precursor HUMAN) hepatocyte growth Q04756 R.VQLSPDLLATL 0.82 factor activator (HGFA_ PEPASPGR.Q preproprotein HUMAN) histidine-rich P04196 R.DGYLFQLLR.I 0.63 glycoprotein (HRG_ precursor HUMAN) hyaluronan-binding Q14520 K.FLNWIK.A 0.82 protein 2 isoform 1 (HABP2_ preproprotein HUMAN) hyaluronan-binding Q14520 K.LKPVDGHCALE 0.61 protein 2 isoform 1 (HABP2_ SK.Y preproprotein HUMAN) hyaluronan-binding Q14520 K.RPGVYTQVTK. 0.74 protein 2 isoform 1 (HABP2_ F preproprotein HUMAN) inactive caspase-12 Q6UXS9 K.AGADTHGRLLQ 0.74 (CASPC_ GNICNDAVTK.A HUMAN) insulin-degrading P14735 K.KIIEKM*ATFE 0.85 enzyme isoform 1 (IDE_ IDEK.R HUMAN) insulin-like growth P35858 R.SFEGLGQLEVL 0.62 factor-binding (ALS_ TLDHNQ.LQEVK. protein HUMAN) A complex acid labile subunit isoform 2 precursor inter-alpha- P19827 K.ELAAQTIKK.S 0.81 trypsin (ITIH1_ inhibitor HUMAN) heavy chain HI isoform a precursor inter-alpha-trypsin P19827 K.GSLVQASEANL 0.71 inhibitor heavy chain (IT1H1__ QAAQDFVR.G HI isoform a HUMAN) precursor inter-alpha-trypsin P19827 K.QLVHHFEIDVD 0.70 inhibitor heavy chain (ITIH1_ IFEPQGISK.L HI isoform a HUMAN) precursor inter-alpha- P19827 K.QYYEGSEIVVA 0.83 trypsin (ITIH1_ GR.I inhibitor HUMAN) heavy chain heparin cofactor 2 P05546 R.LNILNAK.F 0.68 precursor (HEP2_ HUMAN) heparin cofactor 2 P05546 R.NFGYTLR.S 0.64 precursor (HEP2_ HUMAN) heparin cofactor 2 P05546 R.VLKDQVNTFDN 0.63 precursor (HEP2_ IFIAPVGISTAMG HUMAN) M*ISLGLK.G hepatocyte cell Q14CZ8 K.PLLNDSRMLLS 0.61 adhesion molecule (HECAM_ PDQK.V precursor HUMAN) hepatocyte growth Q04756 R.VQLSPDLLATL 0.82 factor activator (HGFA_ PEPASPGR.Q preproprotein HUMAN) histidine-rich P04196 R.DGYLFQLLR.I 0.63 glycoprotein (HRG_ precursor HUMAN) hyaluronan-binding Q14520 K.FLNWIK.A 0.82 protein 2 isoform 1 (HABP2_ preproprotein HUMAN) hyaluronan-binding Q14520 K.LKPVDGHCALE 0.61 protein 2 isoform 1 (HABP2_ SK.Y preproprotein HUMAN) hyaluronan-binding Q14520 K.RPGVYTQVTK. 0.74 protein 2 isoform 1 (HABP2_ F preproprotein HUMAN) inactive caspase-12 Q6UXS9 K.AGADTHGRLLQ 0.74 (CASPC_ GNICNDAVTK.A HUMAN) insulin-degrading P14735 K.KIIEKM*ATFE 0.85 enzyme isoform 1 (IDE_ IDEK.R HUMAN) insulin-like P35858 R.SFEGLGQLEVL 0.62 growth (ALS_ TLDHNQ.LQEVK. factor-binding HUMAN) A protein complex acid labile subunit isoform 2 precursor inter-alpha- P19827 K.ELAAQTIKK.S 0.81 trypsin (ITIH1_ inhibitor HUMAN) heavy chain HI isoform a precursor inter-alpha- P19827 K.GSLVQASEANL 0.71 trypsin (ITIH1_ QAAQDFVR.G inhibitor HUMAN) heavy chain HI isoform a precursor inter-alpha- P19827 K.QLVHHFEIDVD 0.70 trypsin (ITIH1_ IFEPQGISK.L inhibitor HUMAN) heavy chain HI isoform a precursor inter-alpha- P19827 K.QYYEGSEIVVA 0.83 trypsin (ITIH1_ GR.I inhibitor HUMAN) heavy chain H1 isoform a precursor inter-alpha-trypsin P19827 R.EVAFDLEIPKT 0.70 inhibitor heavy chain (ITIH1_ AFISDFAVTADGN HI isoform a HUMAN) AFIGDIK.D precursor inter-alpha-trypsin P19827 R.GMADQDGLKPT 0.63 inhibitor heavy chain (ITIH1_ IDKPSEDSPPLEM HI isoform a HUMAN) *LGPR.R precursor inter-alpha-trypsin P19827 R.GMADQDGLKPT 0.60 inhibitor heavy chain (ITIH1_ IDKPSEDSPPLEM HI isoform a HUMAN) LGPR.R precursor inter-alpha-trypsin P19823 K.FDPAKLDQIES 0.80 inhibitor heavy chain (ITIH2_ VITATSANTQLVL H2 precursor HUMAN) ETLAQM*DDLQDF LSK.D inter-alpha-trypsin P19823 K.KFYNQVSTPLL 0.76 inhibitor heavy chain (ITIH2_ R.N H2 precursor HUMAN) inter-alpha-trypsin P19823 K.NILFVIDVSGS 0.68 inhibitor heavy chain (ITIH2_ M*WGVK.M H2 precursor HUMAN) inter-alpha-trypsin P19823 K.NILFVIDVSGS 0.62 inhibitor heavy chain (ITIH2_ MWGVK.M H2 precursor HUMAN) inter-alpha-trypsin P19823 R.KLGSYEHR.I 0.72 inhibitor heavy chain (ITIH2_ H2 precursor HUMAN) inter-alpha-trypsin P19823 R.LSNENHGIAQ 0.66 inhibitor heavy chain (ITIH2_ R.I H2 precursor HUMAN) inter-alpha-trypsin P19823 R.MATTMIQSK.V 0.60 inhibitor heavy chain (ITIH2_ H2 precursor HUMAN) inter-alpha-trypsin P19823 R.SILQ.M*SLDH 0.63 inhibitor heavy chain (ITIH2_ HIVTPLTSLVIEN H2 precursor HUMAN) EAGDER.M inter-alpha-trypsin P19823 R.SILQMSLDHHI 0.65 inhibitor heavy chain (ITIH2_ VTPLTSLVIENEA H2 precursor HUMAN) GDER.M inter-alpha-trypsin P19823 R.TEVNVLPGAK. 0.69 inhibitor heavy chain (ITIH2_ V H2 precursor HUMAN) inter-alpha-trypsin Q14624 K.NWFVIDK.S 0.68 inhibitor heavy chain (ITIH4_ H4 isoform 1 HUMAN) precursor inter-alpha-trypsin Q14624 K.WKETLFSVMPG 0.65 inhibitor heavy chain (ITIH4_ LK.M H4 isoform 1 HUMAN) precursor inter-alpha-trypsin Q14624 K.YIFHNFM*ER. 0.67 inhibitor heavy chain (ITIH4_ L H4 isoform 1 HUMAN) precursor inter-alpha-trypsin Q14624 R.FAHTVVTSR.V 0.63 inhibitor heavy chain (ITIH4_ H4 isoform 1 HUMAN) precursor inter-alpha-trypsin Q14624 R.FKPTLSQQQK. 0.60 inhibitor heavy chain (ITIH4_ S H4 isoform 1 HUMAN) precursor inter-alpha-trypsin Q14624 R.IHEDSDSALQL 0.64 inhibitor heavy chain (ITIH4_ QDFYQEVANPLLT H4 isoform 1 HUMAN) AVTFEYPSNAVEE precursor VTQNNFR.L inter-alpha-trypsin Q14624 R.MNFRPGVLSS 0.63 inhibitor heavy chain (ITIH4_ R.Q H4 isoform 1 HUMAN) precursor inter-alpha-trypsin Q14624 R.NVHSAGAAGS 0.62 inhibitor heavy chain (ITIH4_ R.M H4 isoform 1 HUMAN) precursor inter-alpha-trypsin Q14624 R.NVHSGSTFFK. 0.75 inhibitor heavy chain (IT1H4_ Y H4 isoform 1 HUMAN) precursor inter-alpha-trypsin Q14624 R.RLGVYELLLK. 0.66 inhibitor heavy chain (ITIH4_ V H4 isoform 1 HUMAN) precursor kallistatin P29622 K.KLELHLPK.F 0.78 precursor (KAIN_ HUMAN) kallistatin P29622 R.EIEEVLTPEML 0.60 precursor (KAIN_ MR.W HUMAN) kininogen-1 isoform 2 P01042 K.AATGECTATVG 0.67 precursor (KNG1_ KR.S HUMAN) kininogen-1 isoform 2 P01042 K.LGQSLDCNAEV 0.72 precursor (KNG1_ YWPWEK.K HUMAN) kininogen-1 isoform 2 P01042 K.YNSQNQSNNQF 0.62 precursor (KNG1_ VLYR.I HUMAN) kininogen-1 isoform 2 P01042 R.QVVAGLNFR.I 0.64 precursor (KNG1_ HUMAN) leucine-rich alpha-2- P02750 K.DLLLPQPDLR. 0.64 glycoprotein (A2GL_ Y precursor HUMAN) leucine-rich alpha-2- P02750 R.LHLEGNKLQVL 0.76 glycoprotein (A2GL_ GK.D precursor HUMAN) leucine-rich alpha-2- P02750 R.TLDLGENQLET 0.61 glycoprotein (A2GL_ LPPDLLR.G precursor HUMAN) lipopolysaccharide- P18428 K.GLQYAAQEGLL 0.82 binding protein (LBP_ ALQSELLR.I precursor HUMAN) lipopolysaccharide- P18428 K.LAEGFPLPLL 0.66 binding protein (LBP_ K.R precursor HUMAN) lumican precursor P51884 K.SLEYLDLSFN 0.65 (LUM_ Q.IAR.L HUMAN) lumican precursor P51884 R.LKEDAVSAAF 0.74 (LUM_ K.G HUMAN) m7GpppX Q96C86 R.IVFENPDPSDG 0.62 diphosphatase (DCPS_ FVLIPDLK.W HUMAN) matrix Q99542 R.VYFFK.G 0.63 metalloproteinase-19 (MMP19_ isoform 1 HUMAN) preproprotein MBT domain- Q05BQ5 K.WFDYLR.E 0.65 containing protein 1 (MBTD1_ HUMAN) monocyte P08571 R.LTVGAAQVPAQ 0.66 differentiation (CD14_ LLVGALR.V antigen CD14 HUMAN) precursor pappalysin-1 Q13219 R.VSFSSPLVAIS 0.66 preproprotein (PAPP1_ GVALR.S HUMAN) phosphatidylinositol- P80108 K.GIVAAFYSGPS 0.71 glycan-specific (PHLD_ LSDKEK.L phospholipase D HUMAN) precursor phosphatidylinositol- P80108 R.WYVPVKDLLGI 0.71 glycan-specific (PHLD_ YEK.L phospholipase D HUMAN) precursor pigment epithelium- P36955 K.LQSLFDSPDFS 0.61 derived factor (PEDF_ K.I precursor HUMAN) pigment epithelium- P36955 R.ALYYDLISSPD 0.72 derived factor (PEDF_ IHGTYK.E precursor HUMAN) plasma kallikrein P03952 R.CLLFSFLPASS 0.60 preproprotein (KLKB1_ INDMEKR.F HUMAN) plasma protease Cl P05155 K.FQPTLLTLPR. 0.70 inhibitor precursor (IC1_ I HUMAN) plasma protease Cl P05155 K.GVTSVSQ.IFH 0.66 inhibitor precursor (IC1_ SPDLAIR.D HUMAN) plasminogen isoform P00747 K.VIPACLPSPNY 0.63 1 precursor (PLMN_ VVADR.T HUMAN) plasminogen isoform P00747 R.FVTWIEGVMR. 0.60 1 precursor (PLMN_ N HUMAN) plasminogen isoform P00747 R.HSIFTPETNP 0.63 1 precursor (PLMN_ R.A HUMAN) platelet basic P02775 K.GKEESLDSDLY 0.70 protein (CXCL7_ AELR.C preproprotein HUMAN) platelet P40197 K.MVLLEQLFLDH 0.66 glycoprotein (GPV_ NALR.G V precursor HUMAN) platelet P40197 R.LVSLDSGLLNS 0.88 glycoprotein (GPV_ LGALTELQFHR.N V precursor HUMAN) pregnancy zone P20742 K.ALLAYAFSLLG 0.66 protein precursor (PZP_ K.Q HUMAN) pregnancy zone P20742 K.DLFHCVSFTLP 0.86 protein precursor (PZP_ R.I HUMAN) pregnancy zone P20742 K.MLQ.ITNTGFE 0.84 protein precursor (PZP_ MK.L HUMAN) pregnancy zone P20742 R.NELIPLIYLEN 0.65 protein precursor (PZP_ PRR.N HUMAN) pregnancy zone P20742 R.SYIFIDEAHIT 0.68 protein precursor (PZP_ QSLTWLSQMQK.D HUMAN) pregnancy-specific P11465 R.SDPVTLNLLHG 0.66 beta-l-glycoprotein 2 (PSG2_ PDLPR.I precursor HUMAN) pregnancy-specific Q16557 R.TLFLFGVTK.Y 0.62 beta-l-glycoprotein 3 (PSG3_ precursor HUMAN) pregnancy-specific Q15238 R.ILILPSVTR.N 0.76 beta-l-glycoprotein 5 (PSG5_ precursor HUMAN) pregnancy-specific Q00889 R.SDPVTLNLLP 0.63 beta-l-glycoprotein 6 (PSG6_ K.L isoform a HUMAN) progesterone- Q8WXW3 R.VLQLEK.Q 0.71 induced-blocking (PIBF1_ factor 1 HUMAN) protein AMBP P02760 R.VVAQGVGIPED 0.60 preproprotein (AMBP_ SIFTMADR.G HUMAN) protein CBFA2T2 043439 R.LTEREWADEWK 0.70 isoform MTGRlb (MTG8R _ HLDHALNCIMEMV HUMAN) EK.T protein FAM98C Q17RN3 R.ALCGGDGAAAL 0.75 (FA98C_ REPGAGLR.L HUMAN) protein NLRC3 Q7RTR2 K.ALM*DLLAGKG 0.92 (NLRC3_ SQGSQAPQALDR. HUMAN) T protein Z-dependent Q9UK55 K.MGDHLALEDYL 0.60 protease inhibitor (ZPI_ TTDLVETWLR.N precursor HUMAN) prothrombin P00734 K.SPQELLCGASL 0.84 preproprotein (THRB_ ISDR.W HUMAN) prothrombin P00734 R.LAVTTHGLPCL 0.62 preproprotein (THRB_ AWASAQAK.A HUMAN) prothrombin P00734 R.SEGSSVNLSPP 0.70 preproprotein (THRB_ LEQCVPDR.G HUMAN) prothrombin P00734 R.SGIECQLWR.S 0.68 preproprotein (THRB_ HUMAN) prothrombin P00734 R.TATSEYQTFFN 0.60 preproprotein (THRB_ PR.T HUMAN) prothrombin P00734 R.VTGWGNLKETW 0.69 preproprotein (THRB_ TANVGK.G HUMAN) putative Q5T013 R.IHLM*AGR.V 0.69 hydroxypyruvate (HYI_ isomerase isoform 1 HUMAN) putative Q5T013 R.IHLMAGR.V 0.66 hydroxypyruvate (HYI _ isomerase isoform 1 HUMAN) ras-like Q92737 R.PAHPALR.L 0.71 protein family (RSLAA_ member 10A HUMAN) precursor ras-related GTP- Q7L523 K.ISNIIK.Q 0.82 binding protein A (RRAGA_ HUMAN) retinol-binding P02753 K.M*KYWGVASFL 0.73 protein 4 precursor (RET4_ QK.G HUMAN) retinol-binding P02753 R.FSGTWYAM*AK 0.63 protein 4 precursor (RET4_ .K HUMAN) retinol-binding P02753 R.LLNLDGTCADS 0.79 protein 4 precursor (RET4_ YSFVFSR.D HUMAN) retinol-binding P02753 R.LLNNWDVCADM 0.77 protein 4 precursor (RET4_ VGTFTDTEDPAKF HUMAN) K.M sex hormone-binding P04278 R.LFLGALPGEDS 0.66 globulin isoform 1 (SHBG _ STSFCLNGLWAQG precursor HUMAN) QR.L sex hormone-binding P04278 K.DDWFMLGLR.D 0.60 globulin isoform 4 (SHBG _ precursor HUMAN) sex hormone-binding P04278 R.SCDVESNPGIF 0.64 globulin isoform 4 (SHBG _ LPPGTQAEFNLR. HUMAN) G precursor sex hormone-binding P04278 R.TWDPEGVIFYG 0.65 globulin isoform 4 (SHBG_ DTNPKDDWFM*LG precursor HUMAN) LR.D sex hormone-binding P04278 R.TWDPEGVIFYG 0.66 globulin isoform 4 (SHBG_ DTNPKDDWFMLGL precursor HUMAN) R.D signal transducer P52630 R.KFCRDIQDPTQ 0.73 and activator of (STAT2_ LAEMIFNLLLEEK transcription 2 HUMAN) .R spectrin beta chain, Q13813 R.NELIRQEKLEQ 0.60 non-erythrocytic 1 (SPTN1_ LAR.R HUMAN) stabilin-1 Q9NY15 R.KNLSER.W 0.88 precursor (STAB1_ HUMAN) succinate- P51649 R.KWYNLMIQNK. 0.88 semialdehyde (SSDH_ D dehydrogenase, HUMAN) mitochondrial tetranectin P05452 K.SRLDTLAQEVA 0.75 precursor (TETN_ LLK.E HUMAN) THAP domain- Q8TBB0 K.RLDVNAAGIWE 0.69 containing (THAP6_ PKK.G protein HUMAN) thyroxine-binding P05543 R.SILFLGK.V 0.79 globulin precursor (THBG_ HUMAN) tripartite motif- Q9C035 R.ELISDLEHRLQ 0.60 containing protein 5 (TRIM5_ GSVM*ELLQGVD HUMAN) GVIK.R vitamin D-binding P02774 K.EDFTSLSLVLY 0.66 protein isoform 1 (VTDB_ SR.K precursor HUMAN) vitamin D-binding P02774 K.ELSSFIDKGQE 0.67 protein isoform 1 (VTDB_ LCADYSENTFTEY precursor HUMAN) K.K vitamin D-binding P02774 K.ELSSFIDKGQE 0.66 protein isoform 1 (VTDB_ LCADYSENTFTEY precursor HUMAN) KK.K vitamin D-binding P02774 K.EVVSLTEACCA 0.65 protein isoform 1 (VTDB_ EGADPDCYDTR.T precursor HUMAN) vitamin D-binding P02774 K.TAMDVFVCTYF 0.84 protein isoform 1 (VTDB_ MPAAQLPELPDVE precursor HUMAN) LPTNKDVCDPGNT K.V vitamin D-binding P02774 R.RTHLPEVFLS 0.69 protein isoform 1 (VTDB_ .KV precursor HUMAN) vitamin D-binding P02774 R.VCSQYAAYGE 0.66 protein isoform 1 (VTDB_ K.K precursor HUMAN) vitronectin precursor P04004 K.LIRDVWGIEGP 0.61 (VTNC_ IDAAFTR.I HUMAN) vitronectin precursor P04004 R.DVWGIEGPIDA 0.63 (VTNC_ AFTR.I HUMAN) vitronectin precursor P04004 R.ERVYFFK.G 0.81 (VTNC_ HUMAN) vitronectin precursor P04004 R.FEDGVLDPDYP 0.64 (VTNC_ R.N HUMAN) vitronectin precursor P04004 R.IYISGM*APRP 0.75 (VTNC_ SLAK.K HUMAN) zinc finger protein P52746 K.TRFLLR.T 0.66 142 (ZN142_ HUMAN)

TABLE-US-00010 TABLE 9 Significant peptides (AUC > 0.6) for X!Tandem only Protein description Uniprot ID (name) Peptide XT_AUC afamin precursor P43652 K.HELTDEELQSLFTNFANVVDK.C 0.65 (AFAM_HUMAN) afamin precursor P43652 R.NPFVFAPTLLTVAVHFEEVAK.S 0.91 (AFAM_HUMAN) alpha-1- P01011 K.ADLSGITGAR.N 0.67 antichymotrypsin (AACT_HUMAN) precursor alpha-1- P01011 K.MEEVEAMLLPETLKR.W 0.60 antichymotrypsin (AACT_HUMAN) precursor alpha-1- P01011 K.WEMPFDPQDTHQSR.F 0.64 antichymotrypsin (AACT_HUMAN) precursor alpha-1- P01011 R.LYGSEAFATDFQDSAAAK.K 0.62 antichymotrypsin (AACT_HUMAN) precursor alpha-1B-glycoprotein P04217 K.HQFLLTGDTQGR.Y 0.72 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 K.NGVAQEPVHLDSPAIK.H 0.63 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 K.SLPAPWLSM*APVSWITPGLK.T 0.72 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 K.VTLTCVAPLSGVDFQLRR.G 0.67 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.C*EGPIPDVTFELLR.E 0.67 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.C*LAPLEGAR.F 0.79 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.CLAPLEGAR.F 0.63 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.GVTFLLR.R 0.69 precursor (A1BG_HUMAN) alpha-1B-glycoprotein P04217 R.LHDNQNGWSGDSAPVELILSDETL 0.60 precursor (A1BG_HUMAN) PAPEFSPEPESGR.A alpha-1B-glycoprotein P04217 R.TPGAAANLELIFVGPQHAGNYR.C 0.62 precursor (A1BG_HUMAN) alpha-2-antiplasmin P08697 K.HQM*DLVATLSQLGLQELFQAPDL 0.61 isoform a precursor (A2AP_HUMAN) R.G alpha-2-antiplasmin P08697 R.LCQDLGPGAFR.L 0.68 isoform a precursor (A2AP_HUMAN) alpha-2-antiplasmin P08697 R.WFLLEQPEIQVAHFPFK.N 0.60 isoform a precursor (A2AP_HUMAN) alpha-2-HS-glycoprotein P02765 K.VWPQQPSGELFEIEIDTLETTCHVL 0.61 preproprotein (FETUA_HUMAN) DPTPVAR.C alpha-2-HS-glycoprotein P02765 R.HTFMGVVSLGSPSGEVSHPR.K 0.68 preproprotein (FETUA_HUMAN) alpha-2-HS-glycoprotein P02765 R.Q*PNCDDPETEEAALVAIDYINQNL 0.69 preproprotein (FETUA_HUMAN) PWGYK.H alpha-2-HS-glycoprotein P02765 R.QPNCDDPETEEAALVAIDYINQNLP 0.64 preproprotein (FETUA_HUMAN) WGYK.H alpha-2-HS-glycoprotein P02765 R.TVVQPSVGAAAGPVVPPCPGR.I 0.64 preproprotein (FETUA_HUMAN) angiotensinogen P01019 K.QPFVQGLALYTPVVLPR.S 0.73 preproprotein (ANGT_HUMAN) angiotensinogen P01019 R.AAM*VGM*LANFLGFR.I 0.62 preproprotein (ANGT_HUMAN) apolipoprotein A-IV P06727 K.LVPFATELHER.L 0.64 precursor (APOA4_HUMAN) apolipoprotein A-IV P06727 R.LLPHANEVSQK.I 0.61 precursor (APOA4_HUMAN) apolipoprotein A-IV P06727 R.SLAPYAQDTQEKLNHQLEGLTFQM 0.70 precursor (APOA4_HUMAN) K.K apolipoprotein B-100 P04114 K.FPEVDVLTK.Y 0.61 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.HINIDQFVR.K 0.70 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.LLSGGNTLHLVSTTK.T 0.66 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.Q*VFLYPEKDEPTYILNIKR.G 0.81 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.QVFLYPEKDEPTYILNIKR.G 0.77 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.SLHMYANR.L 0.83 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.SVSDGIAALDLNAVANK.I 0.62 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.SVSLPSLDPASAKIEGNLIFDPNNYL 0.67 precursor (APOB_HUMAN) PK.E apolipoprotein B-100 P04114 K.TEVIPPLIENR.Q 0.63 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.VLVDHFGYTK.D 0.76 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 R.TSSFALNLPTLPEVKFPEVDVLTK.Y 0.62 precursor (APOB_HUMAN) apolipoprotein C-III P02656 R.GWVTDGFSSLKDYWSTVK.D 0.66 precursor (APOC3_HUMAN) apolipoprotein E P02649 R.GEVQAMLGQSTEELR.V 0.81 precursor (APOE_HUMAN) apolipoprotein E P02649 R.LAVYQAGAR.E 0.63 precursor (APOE_HUMAN) apolipoprotein E P02649 R.LGPLVEQGR.V 0.69 precursor (APOE_HUMAN) attractin isoform 2 O75882 K.LTLTPWVGLR.K 0.69 preproprotein (ATRN_HUMAN) beta-2-glycoprotein 1 P02749 K.FICPLTGLWPINTLK.C 0.63 precursor (APOH_HUMAN) beta-2-glycoprotein 1 P02749 K.TFYEPGEEITYSCKPGYVSR.G 0.62 precursor (APOH_HUMAN) beta-Ala-His Q96KN2 K.MVVSMTLGLHPWIANIDDTQYLA 0.81 dipeptidase precursor (CNDP1_HUMAN) AK.R beta-Ala-His Q96KN2 K.VFQYIDLHQDEFVQTLK.E 0.65 dipeptidase precursor (CNDP1_HUMAN) biotinidase precursor P43251 R.TSIYPFLDFM*PSPQVVR.W 0.79 (BTD_HUMAN) carboxypeptidase N P15169 R.ELMLQLSEFLCEEFR.N 0.61 catalytic chain (CBPN_HUMAN) precursor ceruloplasmin P00450 K.AEEEHLGILGPQLHADVGDKVK.I 0.73 precursor (CERU_HUMAN) ceruloplasmin P00450 K.ALYLQYTDETFR.T 0.64 precursor (CERU_HUMAN) ceruloplasmin P00450 K.DVDKEFYLFPTVFDENESLLLEDN 0.62 precursor (CERU_HUMAN) IR.M ceruloplasmin P00450 K.HYYIGIIETTWDYASDHGEK.K 0.61 precursor (CERU_HUMAN) ceruloplasmin P00450 R.EYTDASFTNRK.E 0.67 precursor (CERU_HUMAN) ceruloplasmin P00450 R.HYYIAAEEIIWNYAPSGIDIFTK.E 0.63 precursor (CERU_HUMAN) ceruloplasmin P00450 R.IYHSHIDAPK.D 0.62 precursor (CERU_HUMAN) ceruloplasmin P00450 R.Q*KDVDKEFYLFPTVFDENESLLLE 0.74 precursor (CERU_HUMAN) DNIR.M ceruloplasmin P00450 R.QKDVDKEFYLFPTVFDENESLLLED 0.65 precursor (CERU_HUMAN) NIR.M ceruloplasmin P00450 R.TYYIAAVEVEWDYSPQR.E 0.90 precursor (CERU_HUMAN) coagulation factor IX P00740 R.SALVLQYLR.V 0.69 preproprotein (FA9_HUMAN) coagulation factor V P12259 K.EFNPLVIVGLSK.D 0.61 precursor (FA5_HUMAN) coagulation factor XII P00748 R.NPDNDIRPWCFVLNR.D 0.65 precursor (FA12_HUMAN) coagulation factor XII P00748 R.VVGGLVALR.G 0.61 precursor (FA12_HUMAN) complement C1q P02746 K.NSLLGMEGANSIFSGFLLFPDMEA.- 0.64 subcomponent subunit (C1QB_HUMAN) B precursor complement C1q P02746 K.VPGLYYFTYHASSR.G 0.63 subcomponent subunit (C1QB_HUMAN) B precursor complement C1q P02747 R.Q*THQPPAPNSLIR.F 0.60 subcomponent subunit (C1QC_HUMAN) C precursor complement C1r P00736 R.LPVANPQACENWLR.G 0.72 subcomponent (C1R_HUMAN) precursor complement C2 P06681 K.NQGILEFYGDDIALLK.L 0.74 isoform 3 (CO2_HUMAN) complement C2 P06681 K.RNDYLDIYAIGVGK.L 0.61 isoform 3 (CO2_HUMAN) complement C2 P06681 R.QPYSYDFPEDVAPALGTSFSHMLG 0.78 isoform 3 (CO2_HUMAN) ATNPTQK.T complement C3 P01024 R.IHWESASLLR.S 0.69 precursor (CO3_HUMAN) complement C4-A P0C0L4 K.FACYYPR.V 0.64 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.LHLETDSLALVALGALDTALYAAGS 0.74 isoform 1 (CO4A_HUMAN) K.S complement C4-A P0C0L4 K.LVNGQSHISLSK.A 0.64 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.M*RPSTDTITVMVENSHGLR.V 0.60 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.MRPSTDTITVMVENSHGLR.V 0.65 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.SCGLHQLLR.G 0.74 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.VGLSGMAIADVTLLSGFHALR.A 0.61 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.YVLPNFEVK.I 0.64 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.ALEILQEEDLIDEDDIPVR.S 0.64 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.ECVGFEAVQEVPVGLVQPASATLY 0.62 isoform 1 (CO4A_HUMAN) DYYNPER.R complement C4-A P0C0L4 R.EELVYELNPLDHR.G 0.66 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.STQDTVIALDALSAYWIASHTTE 0.70 isoform 1 (CO4A_HUMAN) ER.G complement C4-A P0C0L4 R.VGDTLNLNLR.A 0.79 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.VHYTVCIWR.N 0.65 isoform 1 (CO4A_HUMAN) complement C4-B-like P0C0L5 K.GLCVATPVQLR.V 1.00 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 K.KYVLPNFEVK.I 0.60 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 K.VDFTLSSERDFALLSLQVPLKDAK.S 0.74 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 R.EMSGSPASGIPVK.V 0.72 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 R.GCGEQTM*IYLAPTLAASR.Y 0.75 preproprotein (CO4B_HUMAN) complement C4-B-like P0C0L5 R.NGESVKLHLETDSLALVALGALDTA 0.85 preproprotein (CO4B_HUMAN) LYAAGSK.S complement C5 P01031 R.IPLDLVPK.T 0.65 preproprotein (CO5_HUMAN) complement C5 P01031 R.SYFPESWLWEVHLVPR.R 0.63 preproprotein (CO5_HUMAN) complement C5 P01031 R.YGGGFYSTQDTINAIEGLTEYSLL 0.62 preproprotein (CO5_HUMAN) VK.Q complement P13671 K.ENPAVIDFELAPIVDLVR.N 0.63 component C6 (CO6_HUMAN) precursor complement P07357 K.YNPVVIDFEMQPIHEVLR.H 0.61 component C8 alpha (CO8A_HUMAN) chain precursor complement P07357 R.HTSLGPLEAK.R 0.65 component C8 alpha (CO8A_HUMAN) chain precursor complement P07358 K.C*QHEMDQYWGIGSLASGINLFTN 0.61 component C8 beta (CO8B_HUMAN) SFEGPVLDHR.Y chain preproprotein complement P07358 K.SGFSFGFK.I 0.64 component C8 beta (CO8B_HUMAN) chain preproprotein complement P07358 R.DTMVEDLVVLVR.G 0.77 component C8 beta (CO8B_HUMAN) chain preproprotein complement P07360 K.ANFDAQQFAGTWLLVAVGSACR.F 0.63 component C8 gamma (CO8G_HUMAN) chain precursor complement P07360 R.AEATTLHVAPQGTAMAVSTFR.K 0.61 component C8 gamma (CO8G_HUMAN) chain precursor complement P02748 R.DVVLTTTFVDDIK.A 0.73 component C9 (CO9_HUMAN) precursor complement P02748 R.RPWNVASLIYETK.G 0.66 component C9 (CO9_HUMAN) precursor complement factor B P00751 K.ISVIRPSK.G 0.70 preproprotein (CFAB_HUMAN) complement factor B P00751 K.VASYGVKPR.Y 0.63 preproprotein (CFAB_HUMAN) complement factor B P00751 R.DFHINLFQVLPWLK.E 0.68 preproprotein (CFAB_HUMAN) complement factor B P00751 R.DLLYIGK.D 0.63 preproprotein (CFAB_HUMAN) complement factor B P00751 R.GDSGGPLIVHK.R 0.63 preproprotein (CFAB_HUMAN) complement factor B P00751 R.LEDSVTYHCSR.G 0.68 preproprotein (CFAB_HUMAN) complement factor B P00751 R.LPPTTTCQQQK.E 0.68 preproprotein (CFAB_HUMAN) complement factor H P08603 K.CLHPCVISR.E 0.62 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.CTSTGWIPAPR.C 0.74 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.IDVHLVPDR.K 0.66 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.IVSSAMEPDREYHFGQAVR.F 0.67 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.SIDVACHPGYALPK.A 0.67 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.VSVLCQENYLIQEGEEITCKDGR.W 0.63 isoform a precursor (CFAH_HUMAN) complement factor H P08603 K.WSSPPQCEGLPCK.S 0.60 isoform a precursor (CFAH_HUMAN) complement factor H P08603 R.EIMENYNIALR.W 0.61 isoform a precursor (CFAH_HUMAN) complement factor H P08603 R.RPYFPVAVGK.Y 0.83 isoform a precursor (CFAH_HUMAN) complement factor H P08603 R.WQSIPLCVEK.I 0.63 isoform a precursor (CFAH_HUMAN) complement factor I P05156 R.YQIWTTVVDWIHPDLKR.I 0.72 preproprotein (CFAI_HUMAN) corticosteroid-binding P08185 K.AVLQLNEEGVDTAGSTGVTLNLTSK 0.61 globulin precursor (CBG_HUMAN) PIILR.F corticosteroid-binding P08185 R.GLASANVDFAFSLYK.H 0.66 globulin precursor (CBG_HUMAN) fibrinogen alpha chain P02671 K.TFPGFFSPMLGEFVSETESR.G 0.62 isoform alpha-E (FIBA_HUMAN) preproprotein gelsolin isoform b P06396 K.FDLVPVPTNLYGDFFTGDAYVILK.T 0.66 (GELS_HUMAN) gelsolin isoform b P06396 K.QTQVSVLPEGGETPLFK.Q 0.66 (GELS_HUMAN) gelsolin isoform b P06396 K.TPSAAYLWVGTGASEAEK.T 0.71 (GELS_HUMAN) gelsolin isoform b P06396 R.AQPVQVAEGSEPDGFWEALGGK.A 0.67 (GELS_HUMAN) gelsolin isoform b P06396 R.IEGSNKVPVDPATYGQFYGGDSYIIL 0.60 (GELS_HUMAN) YNYR.H gelsolin isoform b P06396 R.VEKFDLVPVPTNLYGDFFTGDAYVI 0.73 (GELS_HUMAN) LK.T gelsolin isoform b P06396 R.VPFDAATLHTSTAMAAQHGMDD 0.63 (GELS_HUMAN) DGTGQK.Q glutathione peroxidase P22352 K.FLVGPDGIPIMR.W 0.60 3 precursor (GPX3_HUMAN) hemopexin precursor P02790 K.ALPQPQNVTSLLGCTH.- 0.63 (HEMO_HUMAN) hemopexin precursor P02790 K.SLGPNSCSANGPGLYLIHGPNLYCY 0.68 (HEMO_HUMAN) SDVEK.L hemopexin precursor P02790 R.DGWHSWPIAHQWPQGPSAVDAA 0.63 (HEMO_HUMAN) FSWEEK.L hemopexin precursor P02790 R.GECQAEGVLFFQGDR.E 0.67 (HEMO_HUMAN) hemopexin precursor P02790 R.GECQAEGVLFFQGDREWFWDLAT 0.67 (HEMO_HUMAN) GTM*K.E hemopexin precursor P02790 R.LEKEVGTPHGIILDSVDAAFICPGSS 0.75 (HEMO_HUMAN) R.L hemopexin precursor P02790 R.LWWLDLK.S 0.62 (HEMO_HUMAN) hemopexin precursor P02790 R.WKNFPSPVDAAFR.Q 0.68 (HEMO_HUMAN) heparin cofactor 2 P05546 K.DQVNTFDNIFIAPVGISTAMGMISL 0.60 precursor (HEP2_HUMAN) GLK.G insulin-like growth P35858 K.ANVFVQLPR.L 0.71 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor insulin-like growth P35858 R.LEALPNSLLAPLGR.L 0.61 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor insulin-like growth P35858 R.LFQGLGK.L 0.68 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor insulin-like growth P35858 R.NLIAAVAPGAFLGLK.A 0.76 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor insulin-like growth P35858 R.TFTPQPPGLER.L 0.73 factor-binding protein (ALS_HUMAN) complex acid labile subunit isoform 2 precursor inter-alpha-trypsin P19827 K.Q*LVHHFEIDVDIFEPQGISK.L 0.69 inhibitor heavy chain (ITIH1_HUMAN) H1 isoform a precursor inter-alpha-trypsin P19827 K.VTFQLTYEEVLK.R 0.61 inhibitor heavy chain (ITIH1_HUMAN) H1 isoform a precursor inter-alpha-trypsin P19827 K.VTFQLTYEEVLKR.N 0.70 inhibitor heavy chain (ITIH1_HUMAN) H1 isoform a precursor inter-alpha-trypsin P19827 R.GIEILNQVQESLPELSNHASILIMLT 0.62 inhibitor heavy chain (ITIH1_HUMAN) DGDPTEGVTDR.S H1 isoform a precursor inter-alpha-trypsin P19827 R.GM*ADQDGLKPTIDKPSEDSPPLE 0.79 inhibitor heavy chain (ITIH1_HUMAN) M*LGPR.R H1 isoform a precursor inter-alpha-trypsin P19827 R.KAAISGENAGLVR.A 0.78 inhibitor heavy chain (ITIH1_HUMAN) H1 isoform a precursor inter-alpha-trypsin P19823 K.AGELEVFNGYFVHFFAPDNLDPI 0.64 inhibitor heavy chain (ITIH2_HUMAN) PK.N H2 precursor inter-alpha-trypsin P19823 K.FYNQVSTPLLR.N 0.68 inhibitor heavy chain (ITIH2_HUMAN) H2 precursor inter-alpha-trypsin P19823 K.VQFELHYQEVK.W 0.68 inhibitor heavy chain (ITIH2_HUMAN) H2 precursor inter-alpha-trypsin P19823 R.ETAVDGELVVLYDVK.R 0.63 inhibitor heavy chain (ITIH2_HUMAN) H2 precursor inter-alpha-trypsin P19823 R.IYLQPGR.L 0.75 inhibitor heavy chain (ITIH2_HUMAN) H2 precursor inter-alpha-trypsin Q06033 R.LWAYLTIEQLLEK.R 0.60 inhibitor heavy chain (ITIH3_HUMAN) H3 preproprotein inter-alpha-trypsin Q14624 K.ITFELVYEELLK.R 0.60 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 K.LQDRGPDVLTATVSGK.L 0.67 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 K.TGLLLLSDPDKVTIGLLFWDGRGEG 0.63 inhibitor heavy chain (ITIH4_HUMAN) LR.L H4 isoform 1 precursor inter-alpha-trypsin Q14624 K.WKETLFSVM*PGLK.M 0.79 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.AISGGSIQIENGYFVHYFAPEGLTT 0.60 inhibitor heavy chain (ITIH4_HUMAN) M*PK.N H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.AISGGSIQIENGYFVHYFAPEGLTT 0.65 inhibitor heavy chain (ITIH4_HUMAN) MPK.N H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.ANTVQEATFQMELPK.K 0.68 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.SFAAGIQALGGTNINDAMLMAVQ 0.64 inhibitor heavy chain (ITIH4_HUMAN) LLDSSNQEER.L H4 isoform 1 precursor inter-alpha-trypsin Q14624 R.VQGNDHSATR.E 0.63 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 1 precursor inter-alpha-trypsin Q14624 K.ITFELVYEELLKR.R 0.60 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 2 precursor inter-alpha-trypsin Q14624 K.VTIGLLFWDGR.G 0.65 inhibitor heavy chain (ITIH4_HUMAN) H4 isoform 2 precursor inter-alpha-trypsin Q14624 R.LWAYLTIQQLLEQTVSASDADQQA 0.68 inhibitor heavy chain (ITIH4_HUMAN) LR.N H4 isoform 2 precursor kallistatin precursor P29622 K.LFHTNFYDTVGTIQLINDHVK.K 0.73 (KAIN_HUMAN) kininogen-1 isoform 2 P01042 K.ENFLFLTPDCK.S 0.64 precursor (KNG1_HUMAN) kininogen-1 isoform 2 P01042 K.IYPTVNCQPLGMISLMK.R 0.64 precursor (KNG1_HUMAN) kininogen-1 isoform 2 P01042 K.KIYPTVNCQPLGMISLMK.R 0.78 precursor (KNG1_HUMAN) kininogen-1 isoform 2 P01042 K.SLWNGDTGECTDNAYIDIQLR.I 0.67 precursor (KNG1_HUMAN) lumican precursor P51884 K.ILGPLSYSK.I 0.60 (LUM_HUMAN) N-acetylmuramoyl-L- Q96PD5 K.EYGVVLAPDGSTVAVEPLLAGLEAG 0.61 alanine amidase (PGRP2_HUMAN) LQGR.R precursor N-acetylmuramoyl-L- Q96PD5 R.EGKEYGVVLAPDGSTVAVEPLLAGL 0.69 alanine amidase (PGRP2_HUMAN) EAGLQGR.R precursor N-acetylmuramoyl-L- Q96PD5 R.Q*NGAALTSASILAQQVWGTLVLL 0.60 alanine amidase (PGRP2_HUMAN) QR.L precursor pigment epithelium- P36955 K.IAQLPLTGSMSIIFFLPLK.V 0.65 derived factor (PEDF_HUMAN) precursor pigment epithelium- P36955 R.SSTSPTTNVLLSPLSVATALSALSLG 0.79 derived factor (PEDF_HUMAN) AEQR.T precursor plasma kallikrein P03952 K.VAEYMDWILEK.T 0.62 preproprotein (KLKB1_HUMAN) plasma kallikrein P03952 R.C*LLFSFLPASSINDMEKR.F 0.60 preproprotein (KLKB1_HUMAN) plasma kallikrein P03952 R.C*QFFSYATQTFHK.A 0.60 preproprotein (KLKB1_HUMAN) plasma kallikrein P03952 R.CLLFSFLPASSINDMEK.R 0.76 preproprotein (KLKB1_HUMAN) plasma protease C1 P05155 R.LVLLNAIYLSAK.W 0.96 inhibitor precursor (IC1_HUMAN) pregnancy zone protein P20742 R.NALFCLESAWNVAK.E 0.67 precursor (PZP_HUMAN) pregnancy zone protein P20742 R.NQGNTWLTAFVLK.T 0.61 precursor (PZP_HUMAN) pregnancy-specific Q00887 R.SNPVILNVLYGPDLPR.I 0.62 beta-1-glycoprotein 9 (PSG9_HUMAN) precursor prenylcysteine oxidase Q9UHG3 K.IAIIGAGIGGTSAAYYLR.Q 0.71 1 precursor (PCYOX_HUMAN) protein AMBP P02760 K.WYNLAIGSTCPWLK.K 0.77 preproprotein (AMBP_HUMAN) protein AMBP P02760 R.TVAACNLPIVR.G 0.66 preproprotein (AMBP_HUMAN) prothrombin P00734 R.IVEGSDAEIGMSPWQVMLFR.K 0.62 preproprotein (THRB_HUMAN) prothrombin P00734 R.RQECSIPVCGQDQVTVAMTPR.S 0.69 preproprotein (THRB_HUMAN) prothrombin P00734 R.TFGSGEADCGLRPLFEK.K 0.61 preproprotein (THRB_HUMAN) retinol-binding protein P02753 R.FSGTWYAMAK.K 0.60 4 precursor (RET4_HUMAN) retinol-binding protein P02753 R.LLNNWDVCADMVGTFTDTEDPAK.F 0.64 4 precursor (RET4_HUMAN) serum amyloid P- P02743 R.GYVIIKPLVWV.- 0.62 component precursor (SAMP_HUMAN) sex hormone-binding P04278 K.VVLSSGSGPGLDLPLVLGLPLQLK.L 0.60 globulin isoform 1 (SHBG_HUMAN) precursor sex hormone-binding P04278 R.TWDPEGVIFYGDTNPKDDWFM*L 0.75 globulin isoform 1 (SHBG_HUMAN) GLR.D precursor sex hormone-binding P04278 R.TWDPEGVIFYGDTNPKDDWFMLG 0.74 globulin isoform 1 (SHBG_HUMAN) LR.D precursor thrombospondin-1 P07996 K.GFLLLASLR.Q 0.70 precursor (TSP1_HUMAN) thyroxine-binding P05543 K.AVLHIGEK.G 0.85 globulin precursor (THBG_HUMAN) thyroxine-binding P05543 K.FSISATYDLGATLLK.M 0.65 globulin precursor (THBG_HUMAN) thyroxine-binding P05543 K.KELELQIGNALFIGK.H 0.61 globulin precursor (THBG_HUMAN) thyroxine-binding P05543 K.MSSINADFAFNLYR.R 0.67 globulin precursor (THBG_HUMAN) transforming growth Q15582 R.LTLLAPLNSVFK.D 0.65 factor-beta-induced (BGH3_HUMAN) protein ig-h3 precursor transthyretin precursor P02766 R.GSPAINVAVHVFR.K 0.67 (TTHY_HUMAN) uncharacterized Q8ND61 K.MPSHLMLAR.K 0.64 protein C3orf20 (CC020_HUMAN) isoform 1 vitamin D-binding P02774 K.ELPEHTVK.L 0.75 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.EYANQFMWEYSTNYGQAPLSLLVS 0.69 protein isoform 1 (VTDB_HUMAN) YTK.S precursor vitamin D-binding P02774 K.HLSLLTTLSNR.V 0.65 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.HQPQEFPTYVEPTNDEICEAFR.K 0.64 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.LAQKVPTADLEDVLPLAEDITNIL 0.73 protein isoform 1 (VTDB_HUMAN) SK.C precursor vitamin D-binding P02774 K.LCDNLSTK.N 0.70 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.LCMAALK.H 0.63 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.SCESNSPFPVHPGTAECCTK.E 0.63 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.SYLSMVGSCCTSASPTVCFLK.E 0.61 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 K.TAMDVFVCTYFM*PAAQLPELPDV 0.61 protein isoform 1 (VTDB_HUMAN) ELPTNK.D precursor vitamin D-binding P02774 K.VLEPTLK.S 0.69 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 R.KFPSGTFEQVSQLVK.E 0.66 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 R.THLPEVFLSK.V 0.62 protein isoform 1 (VTDB_HUMAN) precursor vitamin D-binding P02774 R.TSALSAK.S 0.74 protein isoform 1 (VTDB_HUMAN) precursor vitronectin precursor P04004 R.GQYCYELDEK.A 0.73 (VTNC_HUMAN) vitronectin precursor P04004 R.M*DWLVPATCEPIQSVFFFSGDK.Y 0.64 (VTNC_HUMAN) vitronectin precursor P04004 R.Q*PQFISR.D 0.63 (VTNC_HUMAN)

TABLE-US-00011 TABLE 10 Significant peptides (AUC > 0.6) for both X!Tandem and Sequest Uniprot ID Protein description (name) Peptide XT_AUC S_AUC afamin precursor P43652 K.HFQNLGK.D 0.74 0.61 (AFAM_HUMAN) afamin precursor P43652 R.RHPDLSIPELL 0.67 0.63 (AFAM_HUMAN) R.I afamin precursor P43652 R.TINPAVDHCC 0.66 0.86 (AFAM_HUMAN) K.T alpha-1-antichymotrypsin P01011 KITDLIKDLDSQ 0.71 0.73 precursor (AACT_HUMAN) TMMVLVNYIFF K.A alpha-1-antichymotrypsin P01011 R.DYNLNDILLQ 0.74 0.62 precursor (AACT_HUMAN) LGIEEAFTSK.A alpha-1-antichymotrypsin P01011 R.GTHVDLGLAS 0.76 0.61 precursor (AACT_HUMAN) ANVDFAFSLYK.Q alpha-1B-glycoprotein P04217 K.SLPAPWLSMA 0.71 0.65 precursor (A1BG_HUMAN) PVSWITPGLK.T alpha-2-antiplasmin P08697 K.GFPIKEDFLEQ 0.66 0.69 isoform a precursor (A2AP_HUMAN) SEQLFGAKPVSL TGK.Q alpha-2-antiplasmin P08697 K.HQMDLVATL 0.67 0.60 isoform a precursor (A2AP_HUMAN) SQLGLQELFQAP DLR.G alpha-2-antiplasmin P08697 R.QLTSGPNQEQ 0.66 0.61 isoform a precursor (A2AP_HUMAN) VSPLTLLK.L alpha-2-HS-glycoprotein P02765 R.AQLVPLPPST 0.64 0.63 preproprotein (FETUA_HUMAN) YVEFTVSGTDC VAK.E angiotensinogen P01019 K.DPTFIPAPIQA 0.69 0.69 preproprotein (ANGT_HUMAN) K.T angiotensinogen P01019 R.FM*QAVTGW 0.65 0.65 preproprotein (ANGT_HUMAN) K.T antithrombin-III P01008 K.ANRPFLVFI 0.72 0.60 precursor (ANT3_HUMAN) R.E antithrombin-III P01008 K.GDDITMVLIL 0.69 0.68 precursor (ANT3_HUMAN) PKPEK.S antithrombin-III P01008 R.DIPMNPMCIY 0.63 0.78 precursor (ANT3_HUMAN) R.S apolipoprotein A-IV P06727 K.KLVPFATELH 0.65 0.77 precursor (APOA4_HUMAN) ER.L apolipoprotein A-IV P06727 K.SLAELGGHLD 0.60 0.75 precursor (APOA4_HUMAN) QQVEEFR.R apolipoprotein B-100 P04114 K.ALYWVNGQV 0.61 0.63 precursor (APOB_HUMAN) PDGVSK.V apolipoprotein B-100 P04114 K.FIIPGLK.L 0.64 0.68 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 K.FSVPAGIVIPS 0.63 0.63 precursor (APOB_HUMAN) FQALTAR.F apolipoprotein B-100 P04114 K.IEGNLIFDPNN 0.63 0.65 precursor (APOB_HUMAN) YLPK.E apolipoprotein B-100 P04114 K.LNDLNSVLV 0.91 0.88 precursor (APOB_HUMAN) MPTFHVPFTDL QVPSCK.L apolipoprotein B-100 P04114 K.VELEVPQLCS 0.60 0.61 precursor (APOB_HUMAN) FILK.T apolipoprotein B-100 P04114 K.VNWEEEAAS 0.60 0.73 precursor (APOB_HUMAN) GLLTSLK.D apolipoprotein B-100 P04114 R.ATLYALSHAV 0.78 0.80 precursor (APOB_HUMAN) NNYHK.T apolipoprotein B-100 P04114 R.TGISPLALIK.G 0.64 0.77 precursor (APOB_HUMAN) apolipoprotein B-100 P04114 R.TLQGIPQMIG 0.65 0.66 precursor (APOB_HUMAN) EVIR.K apolipoprotein C-III P02656 K.DALSSVQESQ 0.80 0.69 precursor (APOC3_HUMAN) VAQQAR.G apolipoprotein C-IV P55056 R.DGWQWFWSP 0.63 0.67 precursor (APOC4_HUMAN) STFR.G apolipoprotein E P02649 K.VQAAVGTSA 0.70 0.72 precursor (APOE_HUMAN) APVPSDNH.- apolipoprotein E P02649 R.WELALGR.F 0.88 0.60 precursor (APOE_HUMAN) beta-2-microglobulin P61769 K.SNFLNCYVSG 0.60 0.70 precursor (B2MG_HUMAN) FHPSDIEVDLLK.N bone marrow P13727 R.GGHCVALCT 0.83 0.86 proteoglycan isoform 1 (PRG2_HUMAN) R.G preproprotein carboxypeptidase B2 Q96IY4 R.LVDFYVMPV 0.61 0.65 preproprotein (CBPB2_HUMAN) VNVDGYDYSW K.K carboxypeptidase B2 Q96IY4 R.YTHGHGSETL 0.60 0.68 preproprotein (CBPB2_HUMAN) YLAPGGGDDWI YDLGIK.Y carboxypeptidase N P22792 K.LSNNALSGLP 0.65 0.67 subunit 2 precursor (CPN2_HUMAN) QGVFGK.L carboxypeptidase N P22792 K.TLNLAQNLLA 0.67 0.69 subunit 2 precursor (CPN2_HUMAN) QLPEELFHPLTS LQTLK.L carboxypeptidase N P22792 R.WLNVQLSP 0.74 0.67 subunit 2 precursor (CPN2_HUMAN) R.Q ceruloplasmin precursor P00450 K.GDSVVWYLF 0.90 0.72 (CERU_HUMAN) SAGNEADVHGI YFSGNTYLWR.G ceruloplasmin precursor P00450 K.MYYSAVDPT 0.70 0.82 (CERU_HUMAN) K.D ceruloplasmin precursor P00450 R.GPEEEHLGIL 0.60 0.65 (CERU_HUMAN) GPVIWAEVGDTI R.V ceruloplasmin precursor P00450 R.IDTINLFPATL 0.66 0.70 (CERU_HUMAN) FDAYMVAQNP GEWMLSCQNL NHLK.A ceruloplasmin precursor P00450 R.SGAGTEDSAC 0.88 0.92 (CERU_HUMAN) IPWAYYSTVDQ VKDLYSGLIGPL IVCR.R cholinesterase precursor P06276 K.IFFPGVSEFG 0.70 0.63 (CHLE_HUMAN) K.E cholinesterase precursor P06276 R.AILQSGSFNAP 0.75 0.77 (CHLE_HUMAN) WAVTSLYEAR.N chorionic gonadotropin, P01233 R.VLQGVLPALP 0.60 0.75 beta polypeptide 8 (CGHB_HUMAN) QVVCNYR.D precursor chorionic P01243 R.ISLLLIESWLE 0.83 0.63 somatomammotropin (CSH_HUMAN) PVR.F hormone 2 isoform 2 precursor coagulation factor XII P00748 R.LHEAFSPVSY 0.60 0.66 precursor (FA12_HUMAN) QHDLALLR.L coagulation factor XII P00748 R.TTLSGAPCQP 0.69 0.82 precursor (FA12_HUMAN) WASEATYR.N complement C1q P02745 K.GLFQVVSGG 0.65 0.60 subcomponent subunit A (C1QA_HUMAN) MVLQLQQGDQ precursor VWVEKDPK.K complement C1r P00736 K.VLNYVDWIK 0.80 0.76 subcomponent precursor (C1R_HUMAN) K.E complement C1s P09871 K.SNALDIIFQTD 0.62 0.77 subcomponent precursor (C1S_HUMAN) LTGQK.K complement C4-A P0C0L4 K.EGAIHREELV 0.76 0.75 isoform 1 (CO4A_HUMAN) YELNPLDHR.G complement C4-A P0C0L4 K.ITQVLHFTK.D 0.63 0.62 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 K.SHALQLNNR.Q 0.66 0.71 isoform 1 (CO4A_HUMAN) complement C4-A P0C0L4 R.AVGSGATFSH 0.65 0.60 isoform 1 (CO4A_HUMAN) YYYM*ILSR.G complement C4-A P0C0L4 R.EPFLSCCQFA 0.64 0.72 isoform 1 (CO4A_HUMAN) ESLR.K complement C4-A P0C0L4 R.GHLFLQTDQP 0.63 0.76 isoform 1 (CO4A_HUMAN) IYNPGQR.V complement C4-A P0C0L4 R.GLEEELQFSL 0.68 0.68 isoform 1 (CO4A_HUMAN) GSK.I complement C4-A P0C0L4 R.GSFEFPVGDA 0.67 0.70 isoform 1 (CO4A_HUMAN) VSK.V complement C4-A P0C0L4 R.LLATLCSAEV 0.61 0.71 isoform 1 (CO4A_HUMAN) CQCAEGK.C complement C4-A P0C0L4 R.VQQPDCREPF 0.65 0.83 isoform 1 (CO4A_HUMAN) LSCCQFAESLRK.K complement C4-A P0C0L4 R.YIYGKPVQGV 0.82 0.76 isoform 1 (CO4A_HUMAN) AYVR.F complement C5 P01031 K.ITHYNYLILS 0.66 0.69 preproprotein (CO5_HUMAN) K.G complement C5 P01031 R.ENSLYLTAFT 0.60 0.68 preproprotein (CO5_HUMAN) VIGIR.K complement C5 P01031 R.KAFDICPLVK.I 0.77 0.65 preproprotein (CO5_HUMAN) complement C5 P01031 R.VDDGVASFVL 0.68 0.61 preproprotein (CO5_HUMAN) NLPSGVTVLEFN VK.T complement component P13671 K.TFSEWLESVK 0.94 0.64 C6 precursor (CO6_HUMAN) ENPAVIDFELAP IVDLVR.N complement component P13671 R.IFDDFGTHYF 0.78 0.75 C6 precursor (CO6_HUMAN) TSGSLGGVYDL LYQFSSEELK.N complement component P10643 K.ELSHLPSLYD 0.69 0.71 C7 precursor (CO7_HUMAN) YSAYR.R complement component P10643 R.RYSAWAESV 0.71 0.70 C7 precursor (CO7_HUMAN) TNLPQVIK.Q complement component P07357 K.YNPVVIDFEM* 0.68 0.73 C8 alpha chain precursor (CO8A_HUMAN) QPIHEVLR.H complement component P07358 K.VEPLYELVTA 0.69 0.70 C8 beta chain (CO8B_HUMAN) TDFAYSSTVR.Q preproprotein complement component P07358 R.SLM*LHYEFL 0.61 0.65 C8 beta chain (CO8B_HUMAN) QR.V preproprotein complement component P07360 K.YGFCEAADQF 0.78 0.76 C8 gamma chain (CO8G_HUMAN) HVLDEVRR.- precursor complement component P07360 R.FLQEQGHR.A 0.63 0.69 C8 gamma chain (CO8G_HUMAN) precursor complement component P07360 R.KLDGICWQV 0.75 0.70 C8 gamma chain (CO8G_HUMAN) R.Q precursor complement component P07360 R.SLPVSDSVLS 0.70 0.60 C8 gamma chain (CO8G_HUMAN) GFEQR.V precursor complement component P02748 R.GTVIDVTDFV 0.68 0.69 C9 precursor (CO9_HUMAN) NWASSINDAPV LISQK.L complement factor B P00751 K.NPREDYLDV 0.72 0.77 preproprotein (CFAB_HUMAN) YVFGVGPLVNQ VNINALASK.K complement factor B P00751 R.GDSGGPLIVH 0.60 0.76 preproprotein (CFAB_HUMAN) KR.S complement factor B P00751 R.HVIILMTDGL 0.60 0.64 preproprotein (CFAB_HUMAN) HNM*GGDPITVI DEIR.D complement factor B P00751 R.KNPREDYLDV 0.63 0.63 preproprotein (CFAB_HUMAN) YVFGVGPLVNQ VNINALASK.K complement factor H P08603 K.SCDIPVFMNA 0.62 0.71 isoform a precursor (CFAH_HUMAN) R.T complement factor H P08603 K.SPPEISHGVV 0.88 0.88 isoform a precursor (CFAH_HUMAN) AHMSDSYQYGE EVTYK.C complement factor H P08603 K.TDCLSLPSFE 0.61 0.66 isoform a precursor (CFAH_HUMAN) NAIPMGEKK.D complement factor I P05156 K.RAQLGDLPW 0.71 0.74 preproprotein (CFAI_HUMAN) QVAIK.D complement factor I P05156 K.SLECLHPGT 0.64 0.81 preproprotein (CFAI_HUMAN) K.F complement factor I P05156 R.TMGYQDFAD 0.73 0.75 preproprotein (CFAI_HUMAN) VVCYTQK.A extracellular matrix Q16610 R.ELLALIQLE 0.69 0.65 protein 1 isoform 3 (ECM1_HUMAN) R.E precursor gelsolin isoform a P06396 R.VPEARPNSMV 0.76 0.62 precursor (GELS_HUMAN) VEHPEFLK.A glutathione peroxidase 3 P22352 R.LFWEPMK.V 0.69 0.67 precursor (GPX3_HUMAN) hemopexin precursor P02790 R.DVRDYFMPCP 0.70 0.72 (HEMO_HUMAN) GR.G heparin cofactor 2 P05546 K.DALENIDPAT 0.61 0.65 precursor (HEP2_HUMAN) QMMILNCIYFK.G heparin cofactor 2 P05546 K.GLIKDALENI 0.64 0.64 precursor (HEP2_HUMAN) DPATQMMILNC IYFK.G heparin cofactor 2 P05546 K.QFPILLDFK.T 0.61 0.69 precursor (HEP2_HUMAN) heparin cofactor 2 P05546 R.VLKDQVNTF 0.88 0.75 precursor (HEP2_HUMAN) DNIFIAPVGISTA MGMISLGLK.G insulin-like growth P35858 R.AFWLDVSHN 0.61 0.82 factor-binding protein (ALS_HUMAN) R.L complex acid labile subunit isoform 2 precursor inter-alpha-trypsin P19827 K.ADVQAHGEG 0.61 0.74 inhibitor heavy chain H1 (ITIH1_HUMAN) QEFSITCLVDEE isoform a precursor EMKK.L inter-alpha-trypsin P19827 K.ILGDM*QPGD 0.71 0.63 inhibitor heavy chain H1 (ITIH1_HUMAN) YFDLVLFGTR.V isoform a precursor inter-alpha-trypsin P19827 K.ILGDMQPGDY 0.68 0.60 inhibitor heavy chain H1 (ITIH1_HUMAN) FDLVLFGTR.V isoform a precursor inter-alpha-trypsin P19827 K.NVVFVIDISGS 0.76 0.83 inhibitor heavy chain H1 (ITIH1_HUMAN) MR.G isoform a precursor inter-alpha-trypsin P19827 K.TAFISDFAVT 0.74 0.63 inhibitor heavy chain H1 (ITIH1_HUMAN) ADGNAFIGDIKD isoform a precursor K.V inter-alpha-trypsin P19827 R.GHMLENHVE 0.78 0.80 inhibitor heavy chain H1 (ITIH1_HUMAN) R.L isoform a precursor inter-alpha-trypsin P19827 R.GM*ADQDGL 0.61 0.62 inhibitor heavy chain H1 (ITIH1_HUMAN) KPTIDKPSEDSP isoform a precursor PLEMLGPR.R inter-alpha-trypsin P19827 R.LWAYLTIQEL 0.68 0.62 inhibitor heavy chain H1 (ITIH1_HUMAN) LAK.R isoform a precursor inter-alpha-trypsin P19827 R.NHM*QYEIVI 0.67 0.65 inhibitor heavy chain H1 (ITIH1_HUMAN) K.V isoform a precursor inter-alpha-trypsin P19823 K.AHVSFKPTVA 0.75 0.61 inhibitor heavy chain H2 (ITIH2_HUMAN) QQR.I precursor inter-alpha-trypsin P19823 K.ENIQDNISLFS 0.80 0.93 inhibitor heavy chain H2 (ITIH2_HUMAN) LGM*GFDVDYD precursor FLKR.L inter-alpha-trypsin P19823 K.ENIQDNISLFS 0.63 0.80 inhibitor heavy chain H2 (ITIH2_HUMAN) LGMGFDVDYDF precursor LKR.L inter-alpha-trypsin P19823 K.HLEVDVWVIE 0.61 0.61 inhibitor heavy chain H2 (ITIH2_HUMAN) PQGLR.F precursor inter-alpha-trypsin P19823 K.LWAYLTINQL 0.69 0.62 inhibitor heavy chain H2 (ITIH2_HUMAN) LAER.S precursor inter-alpha-trypsin P19823 R.AEDHFSVIDF 0.65 0.63 inhibitor heavy chain H2 (ITIH2_HUMAN) NQNIR.T precursor inter-alpha-trypsin P19823 R.FLHVPDTFEG 0.66 0.62 inhibitor heavy chain H2 (ITIH2_HUMAN) HFDGVPVISK.G precursor inter-alpha-trypsin Q14624 K.ILDDLSPR.D 0.67 0.65 inhibitor heavy chain H4 (ITIH4_HUMAN) isoform 1 precursor inter-alpha-trypsin Q14624 K.IPKPEASFSP 0.69 0.77 inhibitor heavy chain H4 (ITIH4_HUMAN) R.R isoform 1 precursor inter-alpha-trypsin Q14624 K.SPEQQETVLD 0.63 0.69 inhibitor heavy chain H4 (ITIH4_HUMAN) GNLIIR.Y isoform 1 precursor inter-alpha-trypsin Q14624 K.YIFHNFMER.L 0.66 0.61 inhibitor heavy chain H4 (ITIH4_HUMAN) isoform 1 precursor inter-alpha-trypsin Q14624 R.FSSHVGGTLG 0.69 0.71 inhibitor heavy chain H4 (ITIH4_HUMAN) QFYQEVLWGSP isoform 1 precursor AASDDGRR.T inter-alpha-trypsin Q14624 R.GPDVLTATVS 0.63 0.82 inhibitor heavy chain H4 (ITIH4_HUMAN) GK.L isoform 1 precursor inter-alpha-trypsin Q14624 R.NMEQFQVSVS 0.78 0.60 inhibitor heavy chain H4 (ITIH4_HUMAN) VAPNAK.I isoform 1 precursor inter-alpha-trypsin Q14624 R.RLDYQEGPPG 0.68 0.62 inhibitor heavy chain H4 (ITIH4_HUMAN) VEISCWSVEL.- isoform 1 precursor kallistatin precursor P29622 K.IVDLVSELKK.D 0.75 0.67 (KAIN_HUMAN) kallistatin precursor P29622 R.VGSALFLSHN 0.70 0.74 (KAIN_HUMAN) LK.F kininogen-1 isoform 2 P01042 K.IYPTVNCQPL 0.89 0.62 precursor (KNG1_HUMAN) GM*ISLM*K.R kininogen-1 isoform 2 P01042 K.TVGSDTFYSF 0.61 0.68 precursor (KNG1_HUMAN) K.Y kininogen-1 isoform 2 P01042 R.DIPTNSPELEE 0.61 0.76 precursor (KNG1_HUMAN) TLTHTITK.L kininogen-1 isoform 2 P01042 R.VQVVAGK.K 0.67 0.71 precursor (KNG1_HUMAN) lumican precursor P51884 R.FNALQYLR.L 0.68 0.76 (LUM_HUMAN) macrophage colony- P09603 K.VIPGPPALTLV 0.68 0.60 stimulating factor 1 (CSF1_HUMAN) PAELVR.I receptor precursor monocyte differentiation P08571 K.ITGTMPPLPLE 0.80 0.67 antigen CD14 precursor (CD14_HUMAN) ATGLALSSLR.L N-acetylmuramoyl-L- Q96PD5 K.EFTEAFLGCP 0.62 0.64 alanine amidase (PGRP2_HUMAN) AIHPR.C precursor N-acetylmuramoyl-L- Q96PD5 R.RVINLPLDSM 0.63 0.62 alanine amidase (PGRP2_HUMAN) AAPWETGDTFP precursor DVVAIAPDVR.A phosphatidylinositol- P80108 R.GVFFSVNSWT 0.67 0.78 glycan-specific (PHLD_HUMAN) PDSMSFIYK.A phospholipase D precursor pigment epithelium- P36955 K.EIPDEISILLLGVAHF 0.63 0.61 derived factor precursor (PEDF_HUMAN) K.G pigment epithelium- P36955 K.IAQLPLTGSM*SIIF 0.79 0.61 derived factor precursor (PEDF_HUMAN) FLPLK.V pigment epithelium- P36955 K.TVQAVLTVPK.L 0.75 0.79 derived factor precursor (PEDF_HUMAN) pigment epithelium- P36955 R.ALYYDLISSPDIHGT 0.60 0.73 derived factor precursor (PEDF_HUMAN) YKELLDTVTAPQK.N pigment epithelium- P36955 R.DTDTGALLFIGK.I 0.85 0.62 derived factor precursor (PEDF_HUMAN) plasminogen isoform 1 P00747 R.ELRPWCFTTDPNK 0.70 0.68 precursor (PLMN_HUMAN) R.W plasminogen isoform 1 P00747 R.TECFITGWGETQGT 0.63 0.68 precursor (PLMN_HUMAN) FGAGLLK.E platelet basic protein P02775 K.GTHCNQVEVIATL 0.60 0.61 preproprotein (CXCL7_HUMAN) K.D pregnancy zone protein P20742 K.AVGYLITGYQR.Q 0.87 0.73 precursor (PZP_HUMAN) pregnancy zone protein P20742 R.AVDQSVLLM*KPE 0.64 0.62 precursor (PZP_HUMAN) AELSVSSVYNLLTVK.D pregnancy zone protein P20742 R.IQHPFTVEEFVLP 0.66 0.74 precursor (PZP_HUMAN) K.F pregnancy zone protein P20742 R.NELIPLIYLENPR.R 0.61 0.61 precursor (PZP_HUMAN) protein AMBP P02760 R.AFIQLWAFDAVK.G 0.72 0.67 preproprotein (AMBP_HUMAN) proteoglycan 4 isoform B Q92954 K.GFGGLTGQIVAALS 0.70 0.72 precursor (PRG4_HUMAN) TAK.Y prothrombin preproprotein P00734 K.YGFYTHVFR.L 0.70 0.63 (THRB_HUMAN) prothrombin preproprotein P00734 R.IVEGSDAEIGM*SP 0.63 0.71 (THRB_HUMAN) WQVMLFR.K retinol-binding protein 4 P02753 K.KDPEGLFLQDNIVA 0.67 0.67 precursor (RET4_HUMAN) EFSVDETGQMSATAK.G thyroxine-binding globulin P05543 K.AQWANPFDPSKTE 0.67 0.80 precursor (THBG_HUMAN) DSSSFLIDK.T thyroxine-binding globulin P05543 K.GWVDLFVPK.F 0.67 0.64 precursor (THBG_HUMAN) thyroxine-binding globulin P05543 R.SFM*LLILER.S 0.65 0.68 precursor (THBG_HUMAN) thyroxine-binding globulin P05543 R.SFMLLILER.S 0.64 0.62 precursor (THBG_HUMAN) vitamin D-binding protein P02774 K.EFSHLGKEDFTSLSL 0.74 0.61 isoform 1 precursor (VTDB_HUMAN) VLYSR.K vitamin D-binding protein P02774 K.EYANQFM*WEYST 0.73 0.61 isoform 1 precursor (VTDB_HUMAN) NYGQAPLSLLVSYTK.S vitamin D-binding protein P02774 K.HQPQEFPTYVEPTN 0.67 0.69 isoform 1 precursor (VTDB_HUMAN) DEICEAFRK.D vitamin D-binding protein P02774 K.SYLSM*VGSCCTSA 0.63 0.62 isoform 1 precursor (VTDB_HUMAN) SPTVCFLK.E vitamin D-binding protein P02774 K.TAM*DVFVCTYFM 0.63 0.60 isoform 1 precursor (VTDB_HUMAN) PAAQLPELPDVELPT NK.D vitamin D-binding protein P02774 K.VPTADLEDVLPLAE 0.70 0.71 isoform 1 precursor (VTDB_HUMAN) DITNILSK.C vitronectin precursor P04004 K.AVRPGYPK.L 0.68 0.77 (VTNC_HUMAN) vitronectin precursor P04004 R.MDWLVPATCEPIQ 0.67 0.65 (VTNC_HUMAN) SVFFFSGDK.Y zinc-alpha-2-glycoprotein P25311 K.EIPAWVPFDPAAQI 0.63 0.67 precursor (ZA2G_HUMAN) TK.Q

[0178] The differentially expressed proteins identified by the hypothesis-independent strategy above, not already present in our MRM-MS assay, were candidates for incorporation into the MRM-MS assay. Two additional proteins (AFP, PGH1) of functional interest were also selected for MRM development. Candidates were prioritized by AUC and biological function, with preference give for new pathways. Sequences for each protein of interest, were imported into Skyline software which generated a list of tryptic peptides, m/z values for the parent ions and fragment ions, and an instrument-specific collision energy (McLean et al. Bioinformatics (2010) 26 (7): 966-968; McLean et al. Anal. Chem (2010) 82 (24): 10116-10124).

[0179] The list was refined by eliminating peptides containing cysteines and methionines, and by using the shotgun data to select the charge state(s) and a subset of potential fragment ions for each peptide that had already been observed on a mass spectrometer.

[0180] After prioritizing parent and fragment ions, a list of transitions was exported with a single predicted collision energy. Approximately 100 transitions were added to a single MRM run. For development, MRM data was collected on either a QTRAP 5500 (AB Sciex) or a 6490 QQQ (Agilent). Commercially available human female serum (from pregnant and non-pregnant donors), was depleted and processed to tryptic peptides, as described above, and used to “scan” for peptides of interest. In some cases, purified synthetic peptides were used for further optimization. For development, digested serum or purified synthetic peptides were separated with a 15 min acetonitrile gradient at 100 ul/min on a 2.1×50 mM Poroshell 120 EC-C18 column (Agilent) at 40° C.

[0181] The MS/MS data was imported back into Skyline, where all chromatograms for each peptide were overlayed and used to identify a consensus peak corresponding to the peptide of interest and the transitions with the highest intensities and the least noise. Table 11, contains a list of the most intensely observed candidate transitions and peptides for transfer to the MRM assay.

TABLE-US-00012 TABLE 11 Candidate peptides and transitions for transferring to the MRM assay fragment ion, m/z, Protein Peptide m/z, charge charge, rank area alpha-1-antichymotrypsin K.ADLSGITGAR.N  480.7591++ S[y7]-661.3628+[1] 1437602 G[y6]-574.3307+[2]  637584 T[y4]-404.2252+[3]  350392 L[y8]-774.4468+[4]  191870 G[y3]-303.1775+[5]  150575 I[y5]-517.3093+[6]   97828 alpha-1-antichymotrypsin K.EQLSLLDR.F  487.2693++ S[y5]-603.3461[1]  345602 L[y6]-716.4301[2]  230046 L[y4]-516.3140[3]  143874 D[y2]-290.1459[4]  113381 D[y2]-290.1459[5]  113381 Q[b2]-258.1084[6]   78157 alpha-1-antichymotrypsin K.ITLLSALVETR.T  608.3690++ S[y7]-775.4308+[1] 1059034 L[y8]-888.5149+[2]  541969 T[b2]-215.1390+[3]  408819 L[y9]-1001.5990+[4]  438441 V[y4]-504.2776+[5]  311293 L[y5]-617.3617+[6]  262544 L[b3]-328.2231+[7]  197526 T[y2]-276.1666+[8]  212816 E[y3]-405.2092+[9]  207163 alpha-1-antichymotrypsin R.EIGELYLPK.F  531.2975++ G[y7]-819.4611+[2]  977307 L[y5]-633.3970+[3]  820582 Y[y4]-520.3130+[4]  400762 L[y3]-357.2496+[5]  498958 P[y2]-244.1656+[1] 1320591 I[b2]-243.1339+[6]  303268 G[b3]-300.1554+[7]  305120 alpha-1-antichymotrypsin R.GTHVDLGLASA  742.3794+++ D[y8]-990.4931+[1]  154927 NVDFAFSLYK.Q L[b8]-793.4203+[2]   51068 D[b5]-510.2307+[3]   45310 F[y7]-875.4662+[4]   42630 A[b9]-864.4574+[5]   43355 S[y4]-510.2922+[6]   45310 F[y5]-657.3606+[7]   37330 V[y9]-1089.5615+[8]   32491 G[b7]-680.3362+[9]   38185 Y[y2]-310.1761+[10]   36336 N[b12]-1136.5695+[11]   16389 S[b10]-951.4894+[12]   16365 L[b6]-623.3148+[13]   13687 L[y3]-423.2602+[14]   17156 V[b4]-395.2037+[15]   10964 alpha-1-antichymotrypsin R.NLAVSQVVHK.A  547.8195++ A[y8]-867.5047+[1]  266203  365.5487+++ L[b2]-228.1343+[2]  314232 V[y7]-796.4676+[3]  165231 A[b3]-299.1714+[4]  173694 S[y6]-697.3991+[5]  158512 H[y2]-284.1717+[6]  136431 V[b4]-398.2398+[7]   36099 S[b5]-485.2718+[8]   23836 S[y6]-697.3991+[1]  223443 V[y3]-383.2401+[2]  112952 V[y4]-482.3085+[3]   84872 Q[y5]-610.3671+[4]   30835 inter-alpha-trypsin K.AAISGENAGLVR.A  579.3173++ S[y9]-902.4690+[1]  518001 inhibitor heavy chain H1 G[y8]-815.4370+[2]  326256 N[y6]-629.3729+[3]  296670 S[b4]-343.1976+[4]  258172 inter-alpha-trypsin K.GSLVQASEANL  668.6763+++ A[y7]-806.4155+[1]  304374 inhibitor heavy chain H1 QAAQDFVR.G A[y6]-735.3784+[2]  193844 V[b4]-357.2132+[3]  294094 F[y3]-421.2558+[4]  167816 A[b6]-556.3089+[5]  149216 L[b11]-535.7775++[6]  156882 A[b13]-635.3253++[7]  249287 A[y14]-760.3786++[8]  123723 F[b17]-865.9208++[9]   23057 inter-alpha-trypsin K.TAFISDFAVTAD 1087.0442++ G[y4]-432.2453+[1]   22362 inhibitor heavy chain H1 GNAFIGDIK.D I[y5]-545.3293+[2]    8319 A[b8]-853.4090+[3]    7006 G[y9]-934.4993+[4]    6755 F[y6]-692.3978+[5]    6193 V[b9]-952.4775+[6]    9508 inter-alpha-trypsin K.VTYDVSR.D  420.2165++ Y[y5]-639.3097+[1]  609348 inhibitor heavy chain H1 T[b2]-201.1234+[2]  792556 D[y4]-476.2463+[3]  169546 V[y3]-361.2194+[4]  256946 Y[y5]-320.1585++[5]  110608 S[y2]-262.1510+[6]   50268 Y[b3]-182.5970++[7]   10947 D[b4]-479.2136+[8]   13662 inter-alpha-trypsin R.EVAFDLEIPK.T  580.8135++ P[y2]-244.1656+[1] 2032509 inhibitor heavy chain H1 D[y6]-714.4032+[2]  672749 A[y8]-932.5088+[3]  390837 L[y5]-599.3763+[4]  255527 F[y7]-861.4716+[5]  305087 inter-alpha-trypsin R.LWAYLTIQELLAK.R  781.4531++ W[b2]-300.1707+[1]  602601 inhibitor heavy chain H1 A[b3]-371.2078+[2]  356967 T[y8]-915.5510+[3]  150419 Y[b4]-534.2711+[4]  103449 I[y7]-814.5033+[5]   72044 Q[y6]-701.4192+[6]   66989 L[b5]-647.3552+[7]   99820 E[y5]-573.3606+[8]   44843 inter-alpha-trypsin K.FYNQVSTPLLR.N  669.3642++ S[y6]-686.4196+[1]  367330 inhibitor heavy chain H2 V[y7]-785.4880+[2]  182396 P[y4]-498.3398+[3]  103638 Y[b2]-311.1390+[4]   52172 Q[b4]-553.2405+[5]   54270 N[b3]-425.1819+[6]   34567 inter-alpha-trypsin K.HLEVDVWVIEPQGLR.F  597.3247+++ I[y7]-812.4625+[1]  206996 inhibitor heavy chain H2 P[y5]-570.3358+[2]  303693 E[y6]-699.3784+[3]  126752 P[y5]-285.6715++[4]   79841 inter-alpha-trypsin K.TAGLVR.S  308.6925++ A[b2]-173.0921+[1]  460019 inhibitor heavy chain H2 G[y4]-444.2929+[2]  789068 V[y2]-274.1874+[3]   34333 G[b3]-230.1135+[4]   15169 L[y3]-387.2714+[5]   29020 inter-alpha-trypsin R.IYLQPGR.L  423.7452++ L[y5]-570.3358+[1]  638209 inhibitor heavy chain H2 P[y3]-329.1932+[2]  235194 Y[b2]-277.1547+[3]  266889 Q[y4]-457.2518+[4]  171389 inter-alpha-trypsin R.LSNENHGIAQR.I  413.5461+++ N[y9]-519.7574++[1]  325409 inhibitor heavy chain H2 N[y7]-398.2146++[2]   39521 G[y5]-544.3202+[3]  139598 S[b2]-201.1234+[4]   54786 E[y8]-462.7359++[5]   30623 inter-alpha-trypsin R.SLAPTAAAKR.R  415.2425++ A[y7]-629.3617+[1]  582421 inhibitor heavy chain H2 L[b2]-201.1234+[2]  430584 P[y6]-558.3246+[3]  463815 A[b3]-272.1605+[4]  204183 T[y5]-461.2718+[5]   47301 inter-alpha-trypsin K.EVSFDVELPK.T  581.8032++ P[y2]-244.1656+[1]  132304 inhibitor heavy chain H3 V[b2]-229.1183+[2]   48895 L[y3]-357.2496+[3]   20685 inter-alpha-trypsin K.IQENVR.N  379.7114++ E[y4]-517.2729+[1]  190296 inhibitor heavy chain H3 E[b3]-371.1925+[2]   51697 Q[b2]-242.1499+[3]   54241 N[y3]-388.2303+[4]   21156 V[y2]-274.1874+[5]    8309 inter-alpha-trypsin R.ALDLSLK.Y  380.2342++ D[y5]-575.3399+[1]  687902 inhibitor heavy chain H3 L[b2]-185.1285+[2]  241010 L[y2]-260.1969+[3]   29365 inter-alpha-trypsin R.LIQDAVTGLTVN  972.0258++ V[b6]-640.3665+[1]  139259 inhibitor heavy chain H3 GQITGDK.R G[b8]-798.4356+[2]   53886 G[y7]-718.3730+[3]   12518 pigment epithelium- K.SSFVAPLEK.S  489.2687++ A[y5]-557.3293+[1]   13436 derived factor precursor V[y6]-656.3978+[2]    9350 F[y7]-803.4662+[3]    6672 P[y4]-486.2922+[4]    6753 pigment epithelium- K.TVQAVLTVPK.L  528.3266++ Q[y8]-855.5298+[1]   26719 derived factor precursor V[b2]-201.1234+[2]   21239 Q[y8]-428.2686++[3]   16900 A[y7]-727.4713+[4]    9518 L[y5]-557.3657+[5]    5108 Q[b3]-329.1819+[6]    5450 V[y6]-656.4341+[7]    4391 pigment epithelium- R.ALYYDLISSPDIH  652.6632+++ Y[y15]-886.4305++[1]   78073 derived factor precursor GTYK.E Y[y14]-804.8988++[2]   26148 pigment epithelium- R.DTDTGALLFIGK.I  625.8350++ G[y8]-818.5135+[1]   25553 derived factor precursor T[b2]-217.0819+[2]   22716 T[b4]-217.0819++[3]   22716 L[y5]-577.3708+[4]   11600 I[y3]-317.2183+[5]   11089 A[b6]-561.2151+[6]    6956 pigment epithelium- K.ELLDTVTAPQK.N  607.8350++ T[y5]-544.3089+[1]   17139 derived factor precursor D[y8]-859.4520+[2]   17440 L[y9]-972.5360+[3]   14344 A[y4]-443.2613+[4]   11474 T[y7]-744.4250+[5]   10808 V[y6]-643.3774+[6]    9064 pregnancy-specific beta- K.FQLPGQK.L  409.2320++ L[y5]-542.3297+[1]  116611 1-glycoprotein 1 P[y4]-429.2456+[2]   91769 Q[b2]-276.1343+[3]   93301 pregnancy-specific beta- R.DLYHYITSYVVD  955.4762+++ G[y7]-707.3471+[1]    5376 1-glycoprotein 1 GEIIIYGPAYSGR.E  Y[y8]-870.4104+[2]    3610 P[y6]-650.3257+[3]    2770 I[y9]-983.4945+[4]    3361 pregnancy-specific beta- K.LFIPQITPK.H  528.8262++  P[y6]-683.4087+[1]   39754 1-glycoprotein 11 F[b2]-261.1598+[2]   29966 I[y7]-796.4927+[3]   13162 pregnancy-specific beta- NSATGEESSTSLTIR  776.8761++  E[b7]-689.2737+[1]   11009 1-glycoprotein 11 T[y6]-690.4145+[2]   11284 L[y4]-502.3348+[3]    2265 S[y7]-389.2269++[4]    1200 T[y3]-389.2507+[5]    1200 I[y2]-288.2030+[6]    2248 pregnancy-specific beta- K.FQQSGQNLFIP  617.3317+++  F[y8]-474.2817++[1]   43682 1-glycoprotein 2 QITTK.H G[y12]-680.3852++[2]   24166 S[b4]-491.2249+[3]   23548 Q[b3]-404.1928+[4]   17499 I[y4]-462.2922+[5]   17304 F[b9]-525.7538++[6]   17206 I[b10]-582.2958++[7]   16718 L[b8]-452.2196++[8]   16490 P[y6]-344.2054++[9]   16198 G[b5]-548.2463+[10]   15320 pregnancy-specific beta- IHPSYTNYR  575.7856++ N[b7]-813.3890+[1]   16879 1-glycoprotein 2 Y[b5]-598.2984+[2]   18087 T[y4]-553.2729+[3]    2682 pregnancy-specific beta- FQLSETNR  497.7513++ L[y6]-719.3682+[1]  358059 1-glycoprotein 2 S[y5]-606.2842+[2]  182330 Q[b2]-276.1343+[3]  292482 pregnancy-specific beta- VSAPSGTGHLPGLNPL  506.2755+++ T[b7]-300.6530++[1]   25346 1-glycoprotein 3 H[y8]-860.4989+[2]   12159 H[y8]-430.7531++[3]   15522 pregnancy-specific beta- EDAGSYTLHIVK  666.8433++ Y[b6]-623.2307+[1]   23965 1-glycoprotein 3 Y[y7]-873.5193+[2]   21686 L[b8]-837.3625+[3]    4104 A[b3]-316.1139+[4]    1987 pregnancy-specific beta- R.TLFIFGVTK.Y  513.3051++ F[y7]-811.4713+[1]   62145 1-glycoprotein 4 L[b2]-215.1390+[2]   31687 F[y5]-551.3188+[3]     972 pregnancy-specific beta- NYTYIWWLNGQS 1097.5576++ W[b6]-841.3879+[1]   25756 1-glycoprotein 4 LPVSPR G[y9]-940.5211+[2]   25018 Y[b4]-542.2245+[3]   19778 Q[y8]-883.4996+[4]    6642 P[y2]-272.1717+[5]    5018 pregnancy-specific beta- GVTGYFTFNLYLK  508.2695+++ L[y2]-260.1969+[1]  176797 1-glycoprotein 5 T[y11]-683.8557++[2]  136231 F[b6]-625.2980+[3]   47523 L[y4]-536.3443+[4]   23513 pregnancy-specific beta- SNPVTLNVLYGPD  585.6527+++ Y[y7]-817.4203+[1]   14118 1-glycoprotein 6 LPR G[y6]-654.3570+[2]   10433 P[b3]-299.1350+[3]   87138* P[y5]-299.1714++[4]   77478* P[y5]-597.3355+[5]   68089* pregnancy-specific beta- DVLLLVHNLPQNL  791.7741+++ L[y8]-1017.5516+[3]  141169 1-glycoprotein 7 TGHIWYK G[y6]-803.4199+[5]  115905 W[y3]-496.2554+[6]  108565 P[y11]-678.8566++[7]  105493 V[b2]-215.1026+[1]  239492 L[b3]-328.1867+[2]  204413 N[b8]-904.5251+[4]  121880 pregnancy-specific beta- YGPAYSGR  435.7089++ A[y5]-553.2729+[1]   25743* 1-glycoprotein 7 Y[y4]-482.2358+[2]   25580* P[y6]-650.3257+[3]   10831* S[y3]-319.1724+[4]   10559* G[b2]-221.0921+[5]    7837* pregnancy-specific beta- LQLSETNR  480.7591++ S[b4]-442.2660+[1]   18766 1-glycoprotein 8 L[b3]-355.2340+[2]   12050 Q[b2]-242.1499+[3]    1339 T[b6]-672.3563+[4]    2489 pregnancy-specific beta- K.LFIPQITR.N  494.3029++ P[y5]-614.3620+[1]   53829 1-glycoprotein 9 I[y6]-727.4461+[2]   13731 I[b3]-374.2438+[3]    4178 Q[y4]-517.3093+[4]    2984 pregnancy-specific beta- K.LPIPYITINNLNPR.E  819.4723++ P[b2]-211.1441+[1]   18814* 1-glycoprotein 9 P[b4]-211.1441++[2]   18814* T[b7]-798.4760+[3]   17287* T[y8]-941.5163+[4]   10205* Y[b5]-584.3443+[5]   10136* N[y6]-727.3846+[6]    9511* pregnancy-specific beta- R.SNPVILNVLYGP  589.6648+++ P[y5]-597.3355+[1]    3994 1-glycoprotein 9 DLPR.I Y[y7]-817.4203+[2]    3743 G[y6]-654.3570+[3]    3045 pregnancy-specific beta- DVLLLVHNLPQNL  810.4387+++ P[y7]-960.4614+[1]  120212 1-glycoprotein 9 PGYFWYK V[b2]-215.1026+[2]   65494 L[b3]-328.1867+[3]   54798 pregnancy-specific beta- SENYTYIWWLNG  846.7603+++ W[y15]-834.4488++[1]   14788 1-glycoprotein 9 QSLPVSPGVK P[y4]-200.6314++[2]   19000 Y[y17]-972.5225++[3]    4596 L[b10]-678.8166++[4]    2660 Y[b6]-758.2992+[5]    1705 P[y4]-400.2554+[6]    1847 Pan-PSG ILILPSVTR  506.3317++ P[y5]-559.3198+[1]  484395 L[b2]-227.1754+[2]  102774 L[b4]-227.1754++[3]  102774 I[y7]-785.4880+[4]   90153 I[b3]-340.2595+[5]   45515 L[y6]-672.4039+[6]   40368 thyroxine-binding K.AQWANPFDPSK.T  630.8040++ A[b4]-457.2194+[1]   30802 globulin precursor S[y2]-234.1448+[2]   28255 D[y4]-446.2245+[3]   24933 thyroxine-binding K.AVLHIGEK.G  289.5080+++ I[y4]-446.2609+[1]  220841 globulin precursor H[y5]-292.1636++[2]  303815 H[y5]-583.3198+[3]  133795 V[b2]-171.1128+[4]  166139 L[y6]-348.7056++[5]  823533 thyroxine-binding K.FLNDVK.T  368.2054++ N[y4]-475.2511+[1]  296859 globulin precursor V[y2]-246.1812+[2]  219597 L[b2]-261.1598+[3]   87504 thyroxine-binding K.FSISATYDLGATL  800.4351++ Y[y9]-993.5615+[1]   34111 globulin precursor LK.M G[y6]-602.3872+[2]   17012 D[y8]-830.4982+   45104 S[b2]-235.1077+[4]   15480 thyroxine-binding K.GWVDLFVPK.F  530.7949++ W[b2]-244.1081+[1] 1261810 globulin precursor P[y2]-244.1656+[2] 1261810 V[b7]-817.4243+[3]  517675 V[y7]-817.4818+[4]  517675 D[y6]-718.4134+[5]  306994 F[b6]-718.3559+[6]  306994 V[y3]-343.2340+[7]  112565 V[b3]-343.1765+[8]  112565 thyroxine-binding K.NALALFVLPK.E  543.3395++ A[y7]-787.5076+[1]  198085 globulin precursor L[b3]-299.1714+[2]  199857 P[y2]-244.1656+[3]  129799 L[y8]-900.5917+[4]  111572 L[y6]-716.4705+[5]   88773 F[y5]-603.3865+[6]   54020 L[y3]-357.2496+[7]   43353 thyroxine-binding R.SILFLGK.V  389.2471++ L[y5]-577.3708+[1] 1878736 globulin precursor I[b2]-201.1234+[2]  946031 G[y2]-204.1343+[3]  424248 L[y3]-317.2183+[4]  291162 F[y4]-464.2867+[5]  391171 AFP R.DFNQFSSGEK.N  386.8402+++ N[b3]-189.0764++[1]   42543 S[y4]-210.6081++[2]   21340 G[y3]-333.1769+[3]   53766 N[b3]-377.1456+[4]   58644 F[b2]-263.1026+[5]    5301 AFP K.GYQELLEK.C  490.2584++ E[y5]-631.3661+[1]  110518 L[y4]-502.3235+[2]   74844 E[y2]-276.1554+[3]   42924 E[b4]-478.1932+[4]   20953 AFP K.GEEELQK.Y  416.7060++ E[b2]-187.0713+[1]   37843 E[y4]-517.2980+[2]   56988 AFP K.FIYEIAR.R  456.2529++ I[y3]-359.2401+[1]   34880 I[b2]-261.1598+[2]    7931 AFP R.HPFLYAPTILL  590.3348+++ I[y7]-421.7660++[1]   11471 WAAR.Y L[y6]-365.2239++[2]    5001 A[b6]-365.1896++[3]    5001 L[y6]-729.4406+[4]    3218 F[b3]-382.1874+[5]    6536 A[b6]-729.3719+[6]    3218 AFP R.TFQAITVTK.L  504.7898++ T[b6]-662.3508+[1]   11241 T[y4]-448.2766+[2]    7541 A[b4]-448.2191+[3]    7541 AFP K.LTTLER.G  366.7162++ T[y4]-518.2933+[1]    7836 L[b4]-215.1390++[2]    4205 T[b2]-215.1390+[3]    4205 AFP R.HPQLAVSVILR.V L[y2]-288.2030+[1]    3781 I[y3]-401.2871+[2]    2924 L[b4]-476.2616+[3]    2647 AFP K.LGEYYLQNAFLV  631.6646+++ G[b2]-171.1128+[1]   10790 AYTK.K Y[y3]-411.2238+[2]    2303 F[b10]-600.2902++[3]    1780 Y[b4]-463.2187+[4]    2214 F[y7]-421.2445++[6]    3072 PGH1 R.ILPSVPK.D  377.2471++ P[y5]-527.3188+[1] 5340492 S[y4]-430.2660+[5]  419777 P[y2]-244.1656+[2] 4198508 P[y5]-264.1630++[3] 2771328 L[b2]-227.1754+[4] 2331263 PGH1 K.AEHPTWGDEQL  639.3026+++ E[b9]-512.2120++[1]   64350 FQTTR.L P[b4]-218.1030++[2]   38282 L[b11]-632.7833++[3]  129128 G[y10]-597.7911++[4]   19406 G[b7]-779.3471+[5]   51467 T[y3]-189.1108++[6]   10590 D[y9]-569.2804++[7]   12460 L[y6]-765.4254+[8]    6704 D[b8]-447.6907++[9]    4893 P[b4]-435.1987+[10]    8858 Q[y7]-893.4839+[11]    6101 T[b5]-268.6268++[12]    5456 T[b5]-536.2463+[13]    5549 PGH1 R.LILIGETIK.I  500.3261++ G[y5]-547.3086+[1]    7649 T[y3]-361.2445+[2]    6680 E[y4]-490.2871+[3]    5234 L[y7]-773.4767+[4]    3342 PGH1 R.LQPFNEYR.K  533.7694++ N[b5]-600.3140+[1]   25963 F[b4]-486.2711+[2]    6915 E[y3]-467.2249+[3]   15079 *QTRAP5500 data, all other peak areas are from Agilent 6490

[0182] Next, the top 2-10 transitions per peptide and up to 7 peptides per protein were selected for collision energy (CE) optimization on the Agilent 6490. Using Skyline or MassHunter Qual software, the optimized CE value for each transition was determined based on the peak area or signal to noise. The two transitions with the largest peak areas per peptide and at least two peptides per protein were chosen for the final MRM method. Substitutions of transitions with lower peak areas were made when a transition with a larger peak area had a high background level or had a low m/z value that has more potential for interference.

[0183] Lastly, the retention times of selected peptides were mapped using the same column and gradient as our established sMRM assay. The newly discovered analytes were subsequently added to the sMRM method and used in a further hypothesis-dependent discovery study described in Example 5 below.

[0184] The above method was typical for most proteins. However, in some cases, the differentially expressed peptide identified in the shotgun method did not uniquely identify a protein, for example, in protein families with high sequence identity. In these cases, a MRM method was developed for each family member. Also, let it be noted that, for any given protein, peptides in addition to those found to be significant and fragment ions not observed on the Orbitrap may have been included in MRM optimization and added to the final sMRM method if those yielded the best signal intensities.

Example 5. Study IV to Identify and Confirm Preterm Birth Biomarkers

[0185] A further hypothesis-dependent discovery study was performed with the scheduled MRM assay used in Examples 3 but now augmented with newly discovered analytes from the Example 4. Less robust transitions (from the original 1708 described in Example 1) were removed to improve analytical performance and make room for the newly discovered analytes. Samples included approximately 30 cases and 60 matched controls from each of three gestational periods (early, 17-22 weeks, middle, 23-25 weeks and late, 26-28 weeks). Log transformed peak areas for each transition were corrected for run order and batch effects by regression. The ability of each analyte to separate cases and controls was determined by calculating univariate AUC values from ROC curves. Ranked univariate AUC values (0.6 or greater) are reported for individual gestational age window sample sets (Tables 12, 13, 15) and a combination of the middle and late window (Table 14). Multivariate classifiers were built using different subsets of analytes (described below) by Lasso and Random Forest methods. Lasso significant transitions correspond to those with non-zero coefficients and Random Forest analyze ranking was determined by the Gini importance values (mean decrease in model accuracy if that variable is removed). We report all analytes with non-zero Lasso coefficients (Tables 16-32) and the top 30 analytes from each Random Forest analysis (Tables 33-49). Models were built considering the top univariate 32 or 100 analytes, the single best univariate analyte for the top 50 proteins or all analytes. Lastly 1000 rounds of bootstrap resampling were performed and the nonzero Lasso coefficients or Random Forest Gini importance values were summed for each analyte amongst panels with AUCs of 0.85 or greater.

TABLE-US-00013 TABLE 12 Early Window Individual Stats Transition Protein AUC ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 0.834 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 0.822 FLNWIK_410.7_560.3 HABP2_HUMAN 0.820 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN 0.808 SFRPFVPR_335.9_635.3 LBP_HUMAN 0.800 LIQDAVTGLTVNGQITGDK_972.0_ ITIH3_HUMAN 0.800 798.4 FSVVYAK_407.2_579.4 FETUA_HUMAN 0.796 ITGFLKPGK_320.9_429.3 LBP_HUMAN 0.796 AHYDLR_387.7_288.2 FETUA_HUMAN 0.796 FSVVYAK_407.2_381.2 FETUA_HUMAN 0.795 SFRPFVPR_335.9_272.2 LBP_HUMAN 0.795 DVLLLVHNLPQNLPGYFWYK_810.4_ PSG9_HUMAN 0.794 967.5 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 0.794 QALEEFQK_496.8_680.3 CO8B_HUMAN 0.792 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN 0.792 AHYDLR_387.7_566.3 FETUA_HUMAN 0.791 VFQFLEK_455.8_811.4 CO5_HUMAN 0.786 ITGFLKPGK_320.9_301.2 LBP_HUMAN 0.783 VFQFLEK_455.8_276.2 CO5_HUMAN 0.782 SLLQPNK_400.2_599.4 CO8A_HUMAN 0.781 VQTAHFK_277.5_431.2 CO8A_HUMAN 0.780 SDLEVAHYK_531.3_617.3 CO8B_HUMAN 0.777 SLLQPNK_400.2_358.2 CO8A_HUMAN 0.776 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 0.776 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.774 DISEVVTPR_508.3_787.4 CFAB_HUMAN 0.774 VSEADSSNADWVTK_754.9_533.3 CFAB_HUMAN 0.773 LSSPAVITDK_515.8_743.4 PLMN_HUMAN 0.773 VQEAHLTEDQIFYFPK_655.7_701.4 CO8G_HUMAN 0.772 DVLLLVHNLPQNLPGYFWYK_810.4_ PSG9_HUMAN 0.771 594.3 ALVLELAK_428.8_672.4 INHBE_HUMAN 0.770 FLNWIK_410.7_561.3 HABP2_HUMAN 0.770 LSSPAVITDK_515.8_830.5 PLMN_HUMAN 0.769 LPNNVLQEK_527.8_844.5 AFAM_HUMAN 0.769 VSEADSSNADWVTK_754.9_347.2 CFAB_HUMAN 0.768 HTLNQIDEVK_598.8_951.5 FETUA_HUMAN 0.767 TTSDGGYSFK_531.7_860.4 INHA_HUMAN 0.761 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 0.760 HTLNQIDEVK_598.8_958.5 FETUA_HUMAN 0.760 DISEVVTPR_508.3_472.3 CFAB_HUMAN 0.760 LIQDAVTGLTVNGQITGDK_972.0_ ITIH3_HUMAN 0.759 640.4 EAQLPVIENK_570.8_699.4 PLMN_HUMAN 0.759 SLPVSDSVLSGFEQR_810.9_836.4 CO8G_HUMAN 0.757 AVLHIGEK_289.5_348.7 THBG_HUMAN 0.755 GLQYAAQEGLLALQSELLR_1037.1_ LBP_HUMAN 0.752 929.5 FLQEQGHR_338.8_497.3 CO8G_HUMAN 0.750 LPNNVLQEK_527.8_730.4 AFAM_HUMAN 0.750 AVLHIGEK_289.5_292.2 THBG_HUMAN 0.749 QLYGDTGVLGR_589.8_501.3 CO8G_HUMAN 0.748 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.747 NADYSYSVWK_616.8_769.4 CO5_HUMAN 0.746 GLQYAAQEGLLALQSELLR_1037.1_ LBP_HUMAN 0.746 858.5 SLPVSDSVLSGFEQR_810.9_723.3 CO8G_HUMAN 0.745 IEEIAAK_387.2_531.3 CO5_HUMAN 0.743 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 0.742 WWGGQPLWITATK_772.4_373.2 ENPP2_HUMAN 0.742 FQLSETNR_497.8_605.3 PSG2_HUMAN 0.741 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.741 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 0.740 LQGTLPVEAR_542.3_571.3 CO5_HUMAN 0.740 SGFSFGFK_438.7_732.4 CO8B_HUMAN 0.740 HELTDEELQSLFTNFANVVDK_817.1_ AFAM_HUMAN 0.740 906.5 VQTAHFK_277.5_502.3 CO8A_HUMAN 0.739 YENYTSSFFIR_713.8_293.1 IL12B_HUMAN 0.739 AFTECCVVASQLR_770.9_574.3 CO5_HUMAN 0.736 EAQLPVIENK_570.8_329.2 PLMN_HUMAN 0.734 QALEEFQK_496.8_551.3 CO8B_HUMAN 0.734 DAQYAPGYDK_564.3_813.4 CFAB_HUMAN 0.734 TEFLSNYLTNVDDITLVPGTLGR_ ENPP2_HUMAN 0.734 846.8_600.3 IAIDLFK_410.3_635.4 HEP2_HUMAN 0.733 TASDFITK_441.7_781.4 GELS_HUMAN 0.731 YEFLNGR_449.7_606.3 PLMN_HUMAN 0.731 TVQAVLTVPK_528.3_428.3 PEDF_HUMAN 0.731 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 0.730 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 0.730 TVQAVLTVPK_528.3_855.5 PEDF_HUMAN 0.730 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 0.727 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.727 SDLEVAHYK_531.3_746.4 CO8B_HUMAN 0.726 FLPCENK_454.2_550.2 IL10_HUMAN 0.725 HPWIVHWDQLPQYQLNR_744.0_ KS6A3_HUMAN 0.725 1047.0 AFTECCVVASQLR_770.9_673.4 CO5_HUMAN 0.725 YGLVTYATYPK_638.3_843.4 CFAB_HUMAN 0.724 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 0.724 DAQYAPGYDK_564.3_315.1 CFAB_HUMAN 0.724 QGHNSVFLIK_381.6_260.2 HEMO_HUMAN 0.722 HELTDEELQSLFTNFANVVDK_817.1_ AFAM_HUMAN 0.722 854.4 TLEAQLTPR_514.8_814.4 HEP2_HUMAN 0.721 IEEIAAK_387.2_660.4 CO5_HUMAN 0.721 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.721 IAPQLSTEELVSLGEK_857.5_333.2 AFAM_HUMAN 0.721 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.720 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.719 IAIDLFK_410.3_706.4 HEP2_HUMAN 0.719 FLQEQGHR_338.8_369.2 CO8G_HUMAN 0.719 ALQDQLVLVAAK_634.9_956.6 ANGT_HUMAN 0.718 IEGNLIFDPNNYLPK_874.0_414.2 APOB_HUMAN 0.717 YEFLNGR_449.7_293.1 PLMN_HUMAN 0.717 TASDFITK_441.7_710.4 GELS_HUMAN 0.716 DADPDTFFAK_563.8_825.4 AFAM_HUMAN 0.716 TLLPVSKPEIR_418.3_514.3 CO5_HUMAN 0.716 NADYSYSVWK_616.8_333.2 CO5_HUMAN 0.715 YGLVTYATYPK_638.3_334.2 CFAB_HUMAN 0.715 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN 0.715 HYGGLTGLNK_530.3_759.4 PGAM1_HUMAN 0.714 DFHINLFQVLPWLK_885.5_400.2 CFAB_HUMAN 0.714 NCSFSIIYPVVIK_770.4_555.4 CRHBP_HUMAN 0.714 HPWIVHWDQLPQYQLNR_744.0_ KS6A3_HUMAN 0.712 918.5 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN 0.711 758.0_574.3 ALDLSLK_380.2_185.1 ITIH3_HUMAN 0.711 ALDLSLK_380.2_575.3 ITIH3_HUMAN 0.710 LDFHFSSDR_375.2_611.3 INHBC_HUMAN 0.709 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.707 EVFSKPISWEELLQ_852.9_260.2 FA40A_HUMAN 0.706 IAPQLSTEELVSLGEK_857.5_533.3 AFAM_HUMAN 0.704 LIENGYFHPVK_439.6_343.2 F13B_HUMAN 0.703 NFPSPVDAAFR_610.8_775.4 HEMO_HUMAN 0.703 QLYGDTGVLGR_589.8_345.2 CO8G_HUMAN 0.702 LYYGDDEK_501.7_563.2 CO8A_HUMAN 0.702 FQLSETNR_497.8_476.3 PSG2_HUMAN 0.701 TGVAVNKPAEFTVDAK_549.6_977.5 FLNA_HUMAN 0.700 IPGIFELGISSQSDR_809.9_679.3 CO8B_HUMAN 0.700 TLFIFGVTK_513.3_215.1 PSG4_HUMAN 0.699 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 0.699 QVFAVQR_424.2_473.3 ELNE_HUMAN 0.699 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN 0.699 758.0_623.4 DFNQFSSGEK_386.8_189.1 FETA_HUMAN 0.699 SVSLPSLDPASAK_636.4_473.3 APOB_HUMAN 0.699 GNGLTWAEK_488.3_634.3 C163B_HUMAN 0.698 LYYGDDEK_501.7_726.3 CO8A_HUMAN 0.698 NFPSPVDAAFR_610.8_959.5 HEMO_HUMAN 0.698 FAFNLYR_465.8_565.3 HEP2_HUMAN 0.697 SGFSFGFK_438.7_585.3 CO8B_HUMAN 0.696 DFHINLFQVLPWLK_885.5_543.3 CFAB_HUMAN 0.696 LQGTLPVEAR_542.3_842.5 CO5_HUMAN 0.694 GAVHVVVAETDYQSFAVLYLER_ CO8G_HUMAN 0.694 822.8_863.5 TSESTGSLPSPFLR_739.9_716.4 PSMG1_HUMAN 0.694 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN 0.694 ESDTSYVSLK_564.8_347.2 CRP_HUMAN 0.693 ILDDLSPR_464.8_587.3 ITIH4_HUMAN 0.693 VQEAHLTEDQIFYFPK_655.7_ CO8G_HUMAN 0.692 391.2 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 0.692 DTDTGALLFIGK_625.8_217.1 PEDF_HUMAN 0.692 HFQNLGK_422.2_285.1 AFAM_HUMAN 0.691 NNQLVAGYLQGPNVNLEEK_700.7_ IL1RA_HUMAN 0.691 999.5 IPGIFELGISSQSDR_809.9_849.4 CO8B_HUMAN 0.691 ESDTSYVSLK_564.8_696.4 CRP_HUMAN 0.690 GAVHVVVAETDYQSFAVLYLER_ CO8G_HUMAN 0.690 822.8_580.3 DADPDTFFAK_563.8_302.1 AFAM_HUMAN 0.690 LDFHFSSDR_375.2_464.2 INHBC_HUMAN 0.689 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 0.688 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.687 IQTHSTTYR_369.5_627.3 F13B_HUMAN 0.686 HYFIAAVER_553.3_658.4 FA8_HUMAN 0.686 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN 0.686 DLHLSDVFLK_396.2_366.2 CO6_HUMAN 0.685 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 0.684 AGITIPR_364.2_272.2 IL17_HUMAN 0.684 IAQYYYTFK_598.8_884.4 F13B_HUMAN 0.684 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 0.683 VEPLYELVTATDFAYSSTVR_754.4_ CO8B_HUMAN 0.682 549.3 AGITIPR_364.2_486.3 IL17_HUMAN 0.682 YEVQGEVFTKPQLWP_911.0_293.1 CRP_HUMAN 0.681 APLTKPLK_289.9_357.2 CRP_HUMAN 0.681 YNSQLLSFVR_613.8_508.3 TFR1_HUMAN 0.681 ANDQYLTAAALHNLDEAVK_686.4_ IL1A_HUMAN 0.681 301.1 IQTHSTTYR_369.5_540.3 F13B_HUMAN 0.681 IHPSYTNYR_575.8_598.3 PSG2_HUMAN 0.681 TEFLSNYLTNVDDITLVPGTLGR_ ENPP2_HUMAN 0.681 846.8_699.4 DPTFIPAPIQAK_433.2_461.2 ANGT_HUMAN 0.679 FQSVFTVTR_542.8_623.4 C1QC_HUMAN 0.679 LQVNTPLVGASLLR_741.0_925.6 BPIA1_HUMAN 0.679 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 0.678 HATLSLSIPR_365.6_272.2 VGFR3_HUMAN 0.678 EDTPNSVWEPAK_686.8_315.2 C1S_HUMAN 0.678 TGISPLALIK_506.8_741.5 APOB_HUMAN 0.678 ILPSVPK_377.2_244.2 PGH1_HUMAN 0.676 HATLSLSIPR_365.6_472.3 VGFR3_HUMAN 0.676 QGHNSVFLIK_381.6_520.4 HEMO_HUMAN 0.676 LPATEKPVLLSK_432.6_460.3 HYOU1_HUMAN 0.675 APLTKPLK_289.9_398.8 CRP_HUMAN 0.674 GVTGYFTFNLYLK_508.3_683.9 PSG5_HUMAN 0.673 TFLTVYWTPER_706.9_401.2 ICAM1_HUMAN 0.673 GDTYPAELYITGSILR_885.0_274.1 F13B_HUMAN 0.672 EDTPNSVWEPAK_686.8_630.3 C1S_HUMAN 0.672 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 0.672 VELAPLPSWQPVGK_760.9_342.2 ICAM1_HUMAN 0.671 GPGEDFR_389.2_322.2 PTGDS_HUMAN 0.670 TDAPDLPEENQAR_728.3_843.4 CO5_HUMAN 0.670 GVTGYFTFNLYLK_508.3_260.2 PSG5_HUMAN 0.669 FAFNLYR_465.8_712.4 HEP2_HUMAN 0.669 ITENDIQIALDDAK_779.9_873.5 APOB_HUMAN 0.669 ILNIFGVIK_508.8_790.5 TFR1_HUMAN 0.669 ISQGEADINIAFYQR_575.6_684.4 MMP8_HUMAN 0.668 GDTYPAELYITGSILR_885.0_ F13B_HUMAN 0.668 1332.8 ELLESYIDGR_597.8_710.4 THRB_HUMAN 0.668 FTITAGSK_412.7_576.3 FABPL_HUMAN 0.667 ILDGGNK_358.7_490.2 CXCL5_HUMAN 0.667 GWVTDGFSSLK_598.8_854.4 APOC3_HUMAN 0.667 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.665 IHPSYTNYR_575.8_813.4 PSG2_HUMAN 0.665 ELLESYIDGR_597.8_839.4 THRB_HUMAN 0.665 SDGAKPGPR_442.7_213.6 COLI_HUMAN 0.664 IAQYYYTFK_598.8_395.2 F13B_HUMAN 0.664 SILFLGK_389.2_201.1 THBG_HUMAN 0.664 IEVNESGTVASSSTAVIVSAR_693.0_ PAI1_HUMAN 0.664 545.3 VSAPSGTGHLPGLNPL_506.3_300.7 PSG3_HUMAN 0.664 LLAPSDSPEWLSFDVTGVVR_730.1_ TGFB1_HUMAN 0.664 430.3 YYGYTGAFR_549.3_771.4 TRFL_HUMAN 0.663 TDAPDLPEENQAR_728.3_613.3 CO5_HUMAN 0.663 IEVIITLK_464.8_815.5 CXL11_HUMAN 0.662 ILPSVPK_377.2_227.2 PGH1_HUMAN 0.662 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.661 DYWSTVK_449.7_347.2 APOC3_HUMAN 0.661 IEGNLIFDPNNYLPK_874.0_845.5 APOB_HUMAN 0.661 WILTAAHTLYPK_471.9_407.2 C1R_HUMAN 0.661 WNFAYWAAHQPWSR_607.3_545.3 PRG2_HUMAN 0.661 SILFLGK_389.2_577.4 THBG_HUMAN 0.661 FSLVSGWGQLLDR_493.3_516.3 FA7_HUMAN 0.661 DTDTGALLFIGK_625.8_818.5 PEDF_HUMAN 0.661 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.660 LWAYLTIQELLAK_781.5_371.2 ITIH1_HUMAN 0.660 LLEVPEGR_456.8_356.2 C1S_HUMAN 0.659 ITENDIQIALDDAK_779.9_632.3 APOB_HUMAN 0.659 LTTVDIVTLR_565.8_716.4 IL2RB_HUMAN 0.658 IEVIITLK_464.8_587.4 CXL11_HUMAN 0.658 QLGLPGPPDVPDHAAYHPF_676.7_ ITIH4_HUMAN 0.658 299.2 TLAFVR_353.7_492.3 FA7_HUMAN 0.656 NSDQEIDFK_548.3_294.2 S10A5_HUMAN 0.656 YHFEALADTGISSEFYDNANDLLSK_ CO8A_HUMAN 0.656 940.8_874.5 SEPRPGVLLR_375.2_454.3 FA7_HUMAN 0.655 FLPCENK_454.2_390.2 IL10_HUMAN 0.654 NCSFSIIYPVVIK_770.4_831.5 CRHBP_HUMAN 0.654 SLDFTELDVAAEK_719.4_874.5 ANGT_HUMAN 0.654 ILLLGTAVESAWGDEQSAFR_721.7_ CXA1_HUMAN 0.653 909.4 SVSLPSLDPASAK_636.4_885.5 APOB_HUMAN 0.653 TGISPLALIK_506.8_654.5 APOB_HUMAN 0.653 YNQLLR_403.7_288.2 ENOA_HUMAN 0.653 YEVQGEVFTKPQLWP_911.0_392.2 CRP_HUMAN 0.652 VPGLYYFTYHASSR_554.3_720.3 C1QB_HUMAN 0.650 SLQNASAIESILK_687.4_589.4 IL3_HUMAN 0.650 WILTAAHTLYPK_471.9_621.4 C1R_HUMAN 0.650 GWVTDGFSSLK_598.8_953.5 APOC3_HUMAN 0.650 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.649 QDLGWK_373.7_503.3 TGFB3_HUMAN 0.649 DYWSTVK_449.7_620.3 APOC3_HUMAN 0.648 ALVLELAK_428.8_331.2 INHBE_HUMAN 0.647 QLGLPGPPDVPDHAAYHPF_676.7_ ITIH4_HUMAN 0.646 263.1 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.645 TFLTVYWTPER_706.9_502.3 ICAM1_HUMAN 0.644 FQSVFTVTR_542.8_722.4 C1QC_HUMAN 0.643 DPNGLPPEAQK_583.3_669.4 RET4_HUMAN 0.642 ETLLQDFR_511.3_322.2 AMBP_HUMAN 0.642 IIEVEEEQEDPYLNDR_996.0_777.4 FBLN1_HUMAN 0.641 ELCLDPK_437.7_359.2 IL8_HUMAN 0.641 TPSAAYLWVGTGASEAEK_919.5_ GELS_HUMAN 0.641 849.4 NQSPVLEPVGR_598.3_866.5 KS6A3_HUMAN 0.641 FNAVLTNPQGDYDTSTGK_964.5_ C1QC_HUMAN 0.641 333.2 LLEVPEGR_456.8_686.4 C1S_HUMAN 0.641 FFQYDTWK_567.8_840.4 IGF2_HUMAN 0.640 SPEAEDPLGVER_649.8_670.4 Z512B_HUMAN 0.639 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.639 SGAQATWTELPWPHEK_613.3_793.4 HEMO_HUMAN 0.638 YSHYNER_323.5_581.3 HABP2_HUMAN 0.638 YHFEALADTGISSEFYDNANDLLSK_ CO8A_HUMAN 0.637 940.8_301.1 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.637 YSHYNER_323.5_418.2 HABP2_HUMAN 0.637 YYLQGAK_421.7_327.1 ITIH4_HUMAN 0.636 EVPLSALTNILSAQLISHWK_740.8_ PAI1_HUMAN 0.636 996.6 VPGLYYFTYHASSR_554.3_420.2 C1QB_HUMAN 0.636 AALAAFNAQNNGSNFQLEEISR_789.1_ FETUA_HUMAN 0.636 746.4 ETLLQDFR_511.3_565.3 AMBP_HUMAN 0.635 IVLSLDVPIGLLQILLEQAR_735.1_ UCN2_HUMAN 0.635 503.3 ENPAVIDFELAPIVDLVR_670.7_ CO6_HUMAN 0.635 811.5 LQLSETNR_480.8_355.2 PSG8_HUMAN 0.635 DPDQTDGLGLSYLSSHIANVER_796.4_ GELS_HUMAN 0.635 456.2 NVNQSLLELHK_432.2_656.3 FRIH_HUMAN 0.634 EIGELYLPK_531.3_633.4 AACT_HUMAN 0.634 SPEQQETVLDGNLIIR_906.5_699.3 ITIH4_HUMAN 0.634 NKPGVYTDVAYYLAWIR_677.0_ FA12_HUMAN 0.632 545.3 QNYHQDSEAAINR_515.9_544.3 FRIH_HUMAN 0.632 EKPAGGIPVLGSLVNTVLK_631.4_ BPIB1_HUMAN 0.632 930.6 VTFEYR_407.7_614.3 CRHBP_HUMAN 0.630 DLPHITVDR_533.3_490.3 MMP7_HUMAN 0.630 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN 0.630 ENPAVIDFELAPIVDLVR_670.7_ CO6_HUMAN 0.630 601.4 YGFYTHVFR_397.2_659.4 THRB_HUMAN 0.629 ILDDLSPR_464.8_702.3 ITIH4_HUMAN 0.629 DPNGLPPEAQK_583.3_497.2 RET4_HUMAN 0.629 GSLVQASEANLQAAQDFVR_668.7_ ITIH1_HUMAN 0.629 806.4 FLYHK_354.2_447.2 AMBP_HUMAN 0.627 FNAVLTNPQGDYDTSTGK_964.5_ C1QC_HUMAN 0.627 262.1 LQDAGVYR_461.2_680.3 PD1L1_HUMAN 0.627 INPASLDK_429.2_630.4 C163A_HUMAN 0.626 LEEHYELR_363.5_580.3 PAI2_HUMAN 0.625 VEHSDLSFSK_383.5_468.2 B2MG_HUMAN 0.624 TSDQIHFFFAK_447.6_659.4 ANT3_HUMAN 0.624 ATLSAAPSNPR_542.8_570.3 CXCL2_HUMAN 0.624 YGFYTHVFR_397.2_421.3 THRB_HUMAN 0.624 EANQSTLENFLER_775.9_678.4 IL4_HUMAN 0.623 GQQPADVTGTALPR_705.9_314.2 CSF1_HUMAN 0.623 VELAPLPSWQPVGK_760.9_400.3 ICAM1_HUMAN 0.622 GEVTYTTSQVSK_650.3_750.4 EGLN_HUMAN 0.622 SLQAFVAVAAR_566.8_487.3 IL23A_HUMAN 0.622 HYGGLTGLNK_530.3_301.1 PGAM1_HUMAN 0.622 GPEDQDISISFAWDK_854.4_753.4 DEF4_HUMAN 0.622 YVVISQGLDKPR_458.9_400.3 LRP1_HUMAN 0.621 LWAYLTIQELLAK_781.5_300.2 ITIH1_HUMAN 0.621 SGAQATWTELPWPHEK_613.3_510.3 HEMO_HUMAN 0.621 GTAEWLSFDVTDTVR_848.9_952.5 TGFB3_HUMAN 0.621 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.621 AHQLAIDTYQEFEETYIPK_766.0_ CSH_HUMAN 0.620 634.4 LPATEKPVLLSK_432.6_347.2 HYOU1_HUMAN 0.620 NIQSVNVK_451.3_546.3 GROA_HUMAN 0.620 TAVTANLDIR_537.3_288.2 CHL1_HUMAN 0.619 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.616 QINSYVK_426.2_496.3 CBG_HUMAN 0.616 GFQALGDAADIR_617.3_288.2 TIMP1_HUMAN 0.615 WNFAYWAAHQPWSR_607.3_673.3 PRG2_HUMAN 0.615 NEIWYR_440.7_357.2 FA12_HUMAN 0.615 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.614 YYLQGAK_421.7_516.3 ITIH4_HUMAN 0.614 ALNSIIDVYHK_424.9_774.4 S10A8_HUMAN 0.614 ETPEGAEAKPWYEPIYLGGVFQLEK_ TNFA_HUMAN 0.614 951.1_877.5 LNIGYIEDLK_589.3_837.4 PAI2_HUMAN 0.614 NVNQSLLELHK_432.2_543.3 FRIH_HUMAN 0.613 ILLLGTAVESAWGDEQSAFR_721.7_ CXA1_HUMAN 0.613 910.6 AALAAFNAQNNGSNFQLEEISR_789.1_ FETUA_HUMAN 0.613 633.3 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.613 VGEYSLYIGR_578.8_708.4 SAMP_HUMAN 0.613 DIPHWLNPTR_416.9_373.2 PAPP1_HUMAN 0.612 NEIVFPAGILQAPFYTR_968.5_ ECE1_HUMAN 0.612 357.2 AEHPTWGDEQLFQTTR_639.3_765.4 PGH1_HUMAN 0.612 VEPLYELVTATDFAYSSTVR_754.4_ CO8B_HUMAN 0.611 712.4 DEIPHNDIALLK_459.9_260.2 HABP2_HUMAN 0.611 QINSYVK_426.2_610.3 CBG_HUMAN 0.610 SWNEPLYHLVTEVR_581.6_614.3 PRL_HUMAN 0.610 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.610 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.610 ANDQYLTAAALHNLDEAVK_686.4_ IL1A_HUMAN 0.610 317.2 VRPQQLVK_484.3_609.4 ITIH4_HUMAN 0.609 IPKPEASFSPR_410.2_506.3 ITIH4_HUMAN 0.609 SPEQQETVLDGNLIIR_906.5_685.4 ITIH4_HUMAN 0.609 DDLYVSDAFHK_655.3_704.3 ANT3_HUMAN 0.609 ELPEHTVK_476.8_347.2 VTDB_HUMAN 0.609 FLYHK_354.2_284.2 AMBP_HUMAN 0.608 QRPPDLDTSSNAVDLLFFTDESGDSR_ C1R_HUMAN 0.608 961.5_262.2 DPDQTDGLGLSYLSSHIANVER_796.4_ GELS_HUMAN 0.608 328.1 NEIWYR_440.7_637.4 FA12_HUMAN 0.607 LQLSETNR_480.8_672.4 PSG8_HUMAN 0.606 GQVPENEANVVITTLK_571.3_462.3 CADH1_HUMAN 0.606 FTGSQPFGQGVEHATANK_626.0_ TSP1_HUMAN 0.605 521.2 LEPLYSASGPGLRPLVIK_637.4_ CAA60698 0.605 260.2 QRPPDLDTSSNAVDLLFFTDESGDSR_ C1R_HUMAN 0.604 961.5_866.3 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.604 TSDQIHFFFAK_447.6_512.3 ANT3_HUMAN 0.604 IQHPFTVEEFVLPK_562.0_861.5 PZP_HUMAN 0.603 NKPGVYTDVAYYLAWIR_677.0_ FA12_HUMAN 0.603 821.5 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.603 EIGELYLPK_531.3_819.5 AACT_HUMAN 0.602 LFYADHPFIFLVR_546.6_647.4 SERPH_HUMAN 0.602 AEHPTWGDEQLFQTTR_639.3_569.3 PGH1_HUMAN 0.601 TSYQVYSK_488.2_787.4 C163A_HUMAN 0.601 YTTEIIK_434.2_704.4 C1R_HUMAN 0.601 NVIQISNDLENLR_509.9_402.3 LEP_HUMAN 0.600 AFLEVNEEGSEAAASTAVVIAGR_ ANT3_HUMAN 0.600 764.4_685.4

TABLE-US-00014 TABLE 13 Middle Window Individual Stats Transition Protein AUC SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.738 VFQFLEK_455.8_811.4 CO5_HUMAN 0.709 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.705 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 0.692 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN 0.686 LLAPSDSPEWLSFDVTGVVR_730.1_ TGFB1_HUMAN 0.683 430.3 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.683 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.681 LHEAFSPVSYQHDLALLR_699.4_ FA12_HUMAN 0.681 251.2 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.679 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.677 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 0.675 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.667 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN 0.665 IEEIAAK_387.2_660.4 CO5_HUMAN 0.664 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.664 TLLPVSKPEIR_418.3_514.3 CO5_HUMAN 0.662 ALQDQLVLVAAK_634.9_956.6 ANGT_HUMAN 0.661 TLAFVR_353.7_492.3 FA7_HUMAN 0.661 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.658 VEHSDLSFSK_383.5_468.2 B2MG_HUMAN 0.653 DPTFIPAPIQAK_433.2_461.2 ANGT_HUMAN 0.653 QGHNSVFLIK_381.6_260.2 HEMO_HUMAN 0.650 SLDFTELDVAAEK_719.4_874.5 ANGT_HUMAN 0.650 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 0.649 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.647 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 0.646 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN 0.644 758.0_574.3 QGHNSVFLIK_381.6_520.4 HEMO_HUMAN 0.644 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN 0.643 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.643 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.643 GPITSAAELNDPQSILLR_632.4_ EGLN_HUMAN 0.643 826.5 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.642 TEQAAVAR_423.2_487.3 FA12_HUMAN 0.642 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 0.642 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 0.642 DLHLSDVFLK_396.2_366.2 CO6_HUMAN 0.641 AFTECCVVASQLR_770.9_574.3 CO5_HUMAN 0.640 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 0.639 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 0.639 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.638 HYINLITR_515.3_301.1 NPY_HUMAN 0.637 HFQNLGK_422.2_285.1 AFAM_HUMAN 0.637 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.636 IHPSYTNYR_575.8_813.4 PSG2_HUMAN 0.635 IEEIAAK_387.2_531.3 CO5_HUMAN 0.635 GEVTYTTSQVSK_650.3_750.4 EGLN_HUMAN 0.634 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.634 VVGGLVALR_442.3_784.5 FA12_HUMAN 0.634 SDGAKPGPR_442.7_459.2 COLI_HUMAN 0.634 DVLLLVHNLPQNLTGHIWYK_791.8_ PSG7_HUMAN 0.634 310.2 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 0.633 NKPGVYTDVAYYLAWIR_677.0_ FA12_HUMAN 0.630 821.5 QVFAVQR_424.2_473.3 ELNE_HUMAN 0.630 NHYTESISVAK_624.8_415.2 NEUR1_HUMAN 0.630 IAPQLSTEELVSLGEK_857.5_333.2 AFAM_HUMAN 0.629 IHPSYTNYR_575.8_598.3 PSG2_HUMAN 0.627 EVFSKPISWEELLQ_852.9_260.2 FA40A_HUMAN 0.627 SILFLGK_389.2_201.1 THBG_HUMAN 0.626 IEVIITLK_464.8_587.4 CXL11_HUMAN 0.625 VVGGLVALR_442.3_685.4 FA12_HUMAN 0.624 VVLSSGSGPGLDLPLVLGLPLQLK_ SHBG_HUMAN 0.624 791.5_598.4 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.623 VVLSSGSGPGLDLPLVLGLPLQLK_ SHBG_HUMAN 0.622 791.5_768.5 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.621 LHEAFSPVSYQHDLALLR_699.4_ FA12_HUMAN 0.621 380.2 AHYDLR_387.7_566.3 FETUA_HUMAN 0.620 FSVVYAK_407.2_381.2 FETUA_HUMAN 0.618 ALALPPLGLAPLLNLWAKPQGR_ SHBG_HUMAN 0.618 770.5_256.2 YENYTSSFFIR_713.8_293.1 IL12B_HUMAN 0.617 VELAPLPSWQPVGK_760.9_342.2 ICAM1_HUMAN 0.617 SILFLGK_389.2_577.4 THBG_HUMAN 0.616 ILPSVPK_377.2_227.2 PGH1_HUMAN 0.615 IPSNPSHR_303.2_496.3 FBLN3_HUMAN 0.615 HYFIAAVER_553.3_301.1 FA8_HUMAN 0.615 FSVVYAK_407.2_579.4 FETUA_HUMAN 0.613 VFQFLEK_455.8_276.2 CO5_HUMAN 0.613 IAPQLSTEELVSLGEK_857.5_533.3 AFAM_HUMAN 0.613 ILPSVPK_377.2_244.2 PGH1_HUMAN 0.613 NKPGVYTDVAYYLAWIR_677.0_ FA12_HUMAN 0.613 545.3 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.612 TPSAAYLWVGTGASEAEK_919.5_ GELS_HUMAN 0.612 849.4 ALALPPLGLAPLLNLWAKPQGR_ SHBG_HUMAN 0.612 770.5_457.3 QLGLPGPPDVPDHAAYHPF_676.7_ ITIH4_HUMAN 0.612 299.2 ILDDLSPR_464.8_587.3 ITIH4_HUMAN 0.611 VELAPLPSWQPVGK_760.9_400.3 ICAM1_HUMAN 0.611 DADPDTFFAK_563.8_825.4 AFAM_HUMAN 0.611 NHYTESISVAK_624.8_252.1 NEUR1_HUMAN 0.611 SEPRPGVLLR_375.2_454.3 FA7_HUMAN 0.611 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN 0.611 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.609 LTTVDIVTLR_565.8_716.4 IL2RB_HUMAN 0.608 TQILEWAAER_608.8_761.4 EGLN_HUMAN 0.608 NEPEETPSIEK_636.8_573.3 SOX5_HUMAN 0.608 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN 0.607 758.0_623.4 LQVNTPLVGASLLR_741.0_925.6 BPIA1_HUMAN 0.607 VPSHAVVAR_312.5_345.2 TRFL_HUMAN 0.607 SLCINASAIESILK_687.4_860.5 IL3_HUMAN 0.607 GVTGYFTFNLYLK_508.3_260.2 PSG5_HUMAN 0.605 DFNQFSSGEK_386.8_189.1 FETA_HUMAN 0.605 QLGLPGPPDVPDHAAYHPF_676.7_ ITIH4_HUMAN 0.605 263.1 TLEAQLTPR_514.8_814.4 HEP2_HUMAN 0.604 AFTECCVVASQLR_770.9_673.4 CO5_HUMAN 0.604 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.604 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.603 LWAYLTIQELLAK_781.5_300.2 ITIH1_HUMAN 0.603 GGLFADIASHPWQAAIFAK_667.4_ TPA_HUMAN 0.603 375.2 IPSNPSHR_303.2_610.3 FBLN3_HUMAN 0.603 TDAPDLPEENQAR_728.3_843.4 CO5_HUMAN 0.603 SPQAFYR_434.7_684.4 REL3_HUMAN 0.602 SSNNPHSPIVEEFQVPYNK_729.4_ C1S_HUMAN 0.601 261.2 AHYDLR_387.7_288.2 FETUA_HUMAN 0.600 DGSPDVTTADIGANTPDATK_973.5_ PGRP2_HUMAN 0.600 844.4 SPQAFYR_434.7_556.3 REL3_HUMAN 0.600

TABLE-US-00015 TABLE 14 Middle Late Individual Stats Transition Protein AUC ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 0.656 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.655 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.652 AVYEAVLR_460.8_587.4 PEPD_HUMAN 0.649 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.644 VFQFLEK_455.8_811.4 CO5_HUMAN 0.643 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 0.640 TLAFVR_353.7_492.3 FA7_HUMAN 0.639 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.637 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.637 TEQAAVAR_423.2_487.3 FA12_HUMAN 0.633 QINSYVK_426.2_496.3 CBG_HUMAN 0.633 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.633 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 0.633 ALQDQLVLVAAK_634.9_956.6 ANGT_HUMAN 0.628 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.628 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.628 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.628 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 0.626 QINSYVK_426.2_610.3 CBG_HUMAN 0.625 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 0.625 DPTFIPAPIQAK_433.2_461.2 ANGT_HUMAN 0.625 AVYEAVLR_460.8_750.4 PEPD_HUMAN 0.623 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 0.623 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 0.623 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.622 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 0.622 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 0.621 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 0.621 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 0.620 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.619 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.619 SLDFTELDVAAEK_719.4_874.5 ANGT_HUMAN 0.618 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN 0.618 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.618 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 0.615 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 0.615 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 0.613 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN 0.612 GYQELLEK_490.3_631.4 FETA_HUMAN 0.612 VPLALFALNR_557.3_917.6 PEPD_HUMAN 0.611 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.611 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.608 WSAGLTSSQVDLYIPK_883.0_357.2 CBG_HUMAN 0.608 ITQDAQLK_458.8_702.4 CBG_HUMAN 0.608 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.607 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.607 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.606 LWAYLTIQELLAK_781.5_300.2 ITIH1_HUMAN 0.606 VVGGLVALR_442.3_784.5 FA12_HUMAN 0.605 AQPVQVAEGSEPDGFWEALGGK_758.0_623.4 GELS_HUMAN 0.603 SVVLIPLGAVDDGEHSCINEK_703.0_798.4 CNDP1_HUMAN 0.603 SETEIHQGFQHLHQLFAK_717.4_318.1 CBG_HUMAN 0.603 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN 0.603 IEVIITLK_464.8_587.4 CXL11_HUMAN 0.602 ITQDAQLK_458.8_803.4 CBG_HUMAN 0.602 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 0.601 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN 0.601 LTTVDIVTLR_565.8_716.4 IL2RB_HUMAN 0.600 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.600

TABLE-US-00016 TABLE 15 Late Window Individual Stats Transition Protein AUC AVYEAVLR_460.8_587.4 PEPD_HUMAN 0.724 AEIEYLEK_497.8_552.3 LYAM1_HUMAN 0.703 QINSYVK_426.2_496.3 CBG_HUMAN 0.695 AVYEAVLR_460.8_750.4 PEPD_HUMAN 0.693 AALAAFNAQNNGSNFQLEEISR_ FETUA_HUMAN 0.684 789.1_746.4 QINSYVK_426.2_610.3 CBG_HUMAN 0.681 VPLALFALNR_557.3_620.4 PEPD_HUMAN 0.678 VGVISFAQK_474.8_580.3 TFR2_HUMAN 0.674 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 0.670 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.670 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 0.660 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 0.660 TSYQVYSK_488.2_787.4 C163A_HUMAN 0.657 ITQDAQLK_458.8_702.4 CBG_HUMAN 0.652 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 0.650 ALEQDLPVNIK_620.4_798.5 CNDP1_HUMAN 0.650 VFQYIDLHQDEFVQTLK_708.4_375.2 CNDP1_HUMAN 0.650 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 0.648 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 0.647 VLSSIEQK_452.3_691.4 1433S_HUMAN 0.647 YSHYNER_323.5_418.2 HABP2_HUMAN 0.646 ILDGGNK_358.7_603.3 CXCL5_HUMAN 0.645 GTYLYNDCPGPGQDTDCR_697.0_666.3 TNR1A_HUMAN 0.645 AEIEYLEK_497.8_389.2 LYAM1_HUMAN 0.645 TLPFSR_360.7_506.3 LYAM1_HUMAN 0.645 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 0.644 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.644 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN 0.644 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.642 TASDFITK_441.7_781.4 GELS_HUMAN 0.641 SETEIHQGFQHLHQLFAK_717.4_447.2 CBG_HUMAN 0.640 SPQAFYR_434.7_556.3 REL3_HUMAN 0.639 TAVTANLDIR_537.3_288.2 CHL1_HUMAN 0.636 VPLALFALNR_557.3_917.6 PEPD_HUMAN 0.636 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN 0.633 SETEIHQGFQHLHQLFAK_717.4_318.1 CBG_HUMAN 0.633 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.633 GYQELLEK_490.3_631.4 FETA_HUMAN 0.633 AYSDLSR_406.2_375.2 SAMP_HUMAN 0.633 SVVLIPLGAVDDGEHSCINEK_ CNDP1_HUMAN 0.632 703.0_798.4 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 0.631 WSAGLTSSQVDLYIPK_883.0_515.3 CBG_HUMAN 0.631 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.628 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN 0.626 758.0_574.3 AGITIPR_364.2_486.3 IL17_HUMAN 0.626 AEVIWTSSDHQVLSGK_586.3_300.2 PD1L1_HUMAN 0.625 TEQAAVAR_423.2_487.3 FA12_HUMAN 0.625 NHYTESISVAK_624.8_415.2 NEUR1_HUMAN 0.625 WSAGLTSSQVDLYIPK_883.0_357.2 CBG_HUMAN 0.623 YSHYNER_323.5_581.3 HABP2_HUMAN 0.623 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.621 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.620 SVVLIPLGAVDDGEHSCINEK_ CNDP1_HUMAN 0.620 703.0_286.2 TLAFVR_353.7_492.3 FA7_HUMAN 0.619 AVDIPGLEAATPYR_736.9_286.1 TENA_HUMAN 0.619 TEFLSNYLTNVDDITLVPGTLGR_ ENPP2_HUMAN 0.618 846.8_600.3 YWGVASFLQK_599.8_849.5 RET4_HUMAN 0.618 TPSAAYLWVGTGASEAEK_919.5_428.2 GELS_HUMAN 0.618 DPNGLPPEAQK_583.3_669.4 RET4_HUMAN 0.617 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 0.616 SPQAFYR_434.7_684.4 REL3_HUMAN 0.616 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 0.615 ALNHLPLEYNSALYSR_621.0_538.3 C06_HUMAN 0.615 IEVNESGTVASSSTAVIVSAR_ PAI1_HUMAN 0.615 693.0_545.3 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.615 LWAYLTIQELLAK_781.5_371.2 ITIH1_HUMAN 0.613 SYTITGLQPGTDYK_772.4_352.2 FINC_HUMAN 0.612 GAVHVVVAETDYQSFAVLYLER_ CO8G_HUMAN 0.612 822.8_863.5 FQLPGQK_409.2_276.1 PSG1_HUMAN 0.612 ILDGGNK_358.7_490.2 CXCL5_HUMAN 0.611 DYWSTVK_449.7_620.3 APOC3_HUMAN 0.611 AGLLRPDYALLGHR_518.0_595.4 PGRP2_HUMAN 0.611 ALNFGGIGVVVGHELTHAFDDQGR_ ECE1_HUMAN 0.611 837.1_360.2 GYQELLEK_490.3_502.3 FETA_HUMAN 0.611 HATLSLSIPR_365.6_472.3 VGFR3_HUMAN 0.610 SVPVTKPVPVTKPITVTK_631.1_658.4 Z512B_HUMAN 0.610 FQLPGQK_409.2_429.2 PSG1_HUMAN 0.610 IYLQPGR_423.7_329.2 ITIH2_HUMAN 0.610 TLNAYDHR_330.5_312.2 PAR3_HUMAN 0.609 DPNGLPPEAQK_583.3_497.2 RET4_HUMAN 0.609 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 0.609 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.608 GAVHVVVAETDYQSFAVLYLER_ CO8G_HUMAN 0.608 822.8_580.3 VPSHAVVAR_312.5_515.3 TRFL_HUMAN 0.608 YWGVASFLQK_599.8_350.2 RET4_HUMAN 0.608 EWVAIESDSVQPVPR_856.4_468.3 CNDP1_HUMAN 0.607 LQDAGVYR_461.2_680.3 PD1L1_HUMAN 0.607 DLYHYITSYVVDGEIIIYGPAYSGR_ PSG1_HUMAN 0.607 955.5_650.3 LWAYLTIQELLAK_781.5_300.2 ITIH1_HUMAN 0.606 ITENDIQIALDDAK_779.9_632.3 APOB_HUMAN 0.606 SYTITGLQPGTDYK_772.4_680.3 FINC_HUMAN 0.606 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.605 IYLQPGR_423.7_570.3 ITIH2_HUMAN 0.605 YNCILLR_403.7_529.4 ENOA_HUMAN 0.605 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 0.605 WWGGQPLWITATK_772.4_373.2 ENPP2_HUMAN 0.605 TASDFITK_441.7_710.4 GELS_HUMAN 0.605 EWVAIESDSVQPVPR_856.4_486.2 CNDP1_HUMAN 0.605 YEFLNGR_449.7_606.3 PLMN_HUMAN 0.604 SNPVTLNVLYGPDLPR_585.7_654.4 PSG6_HUMAN 0.604 ITQDAQLK_458.8_803.4 CBG_HUMAN 0.603 LTTVDIVTLR_565.8_716.4 IL2RB_HUMAN 0.602 FNAVLTNPQGDYDTSTGK_ C1QC_HUMAN 0.602 964.5_262.1 ITGFLKPGK_320.9_301.2 LBP_HUMAN 0.601 DYWSTVK_449.7_347.2 APOC3_HUMAN 0.601 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 0.601 GWVTDGFSSLK_598.8_953.5 APOC3_HUMAN 0.601 YYGYTGAFR_549.3_771.4 TRFL_HUMAN 0.601 ELPEHTVK_476.8_347.2 VTDB_HUMAN 0.601 FTFTLHLETPKPSISSSNLNPR_ PSG1_HUMAN 0.601 829.4_874.4 DLYHYITSYVVDGEIIIYGPAYSGR_ PSG1_HUMAN 0.601 955.5_707.3 SPQAFYR_434.7_684.4 REL3_HUMAN 0.616 TPSAAYLWVGTGASEAEK_ GELS_HUMAN 0.615 919.5_849.4 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 0.615 IEVNESGTVASSSTAVIVSAR_ PAI1_HUMAN 0.615 693.0_545.3 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.615 LWAYLTIQELLAK_781.5_371.2 ITIH1_HUMAN 0.613 SYTITGLQPGTDYK_772.4_352.2 FINC_HUMAN 0.612 GAVHVVVAETDYQSFAVLYLER_ CO8G_HUMAN 0.612 822.8_863.5 FQLPGQK_409.2_276.1 PSG1_HUMAN 0.612 DLYHYITSYVVDGEIIIYGPAYSGR_ PSG1_HUMAN 0.601 955.5_707.3

TABLE-US-00017 TABLE 16 Lasso Early 32 Coef- Variable Protein ficient LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 9.53 VQTAHFK_277.5_431.2 CO8A_HUMAN 9.09 FLNWIK_410.7_560.3 HABP2_HUMAN 6.15 ITGFLKPGK_320.9_429.3 LBP_HUMAN 5.29 ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 3.83 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 3.41 DISEVVTPR_508.3_787.4 CFAB_HUMAN 0.44 AHYDLR_387.7_288.2 FETUA_HUMAN 0.1

TABLE-US-00018 TABLE 17 Lasso Early 100 Coef- Variable Protein ficient LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN 6.56 972.0_798.4 ALNHLPLEYNSALYSR_ CO6_HUMAN 6.51 621.0_538.3 VQTAHFK_277.5_431.2 CO8A_HUMAN 4.51 NIQSVNVK_451.3_674.4 GROA_HUMAN 3.12 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 2.68 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 2.56 AVLHIGEK_289.5_292.2 THBG_HUMAN 2.11 FLNWIK_410.7_560.3 HABP2_HUMAN 1.85 ITGFLKPGK_320.9_429.3 LBP_HUMAN 1.36 DALSSVQESQVAQQAR_ APOC3_HUMAN 1.3 573.0_672.4 DALSSVQESQVAQQAR_ APOC3_HUMAN 0.83 573.0_502.3 FLPCENK_454.2_550.2 IL10_HUMAN 0.39 ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 0.3 TEFLSNYLTNVDDITLVPGTLGR_ ENPP2_HUMAN 0.29 846.8_600.3 VSEADSSNADWVTK_754.9_347.2 CFAB_HUMAN 0.27 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 0.13 TGVAVNKPAEFTVDAK_ FLNA_HUMAN 0.04 549.6_258.1 TASDFITK_441.7_781.4 GELS_HUMAN −5.91 LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN 6.56 972.0_798.4

TABLE-US-00019 TABLE 18 Lasso Protein Early Window Coef- Variable Protein ficient ALNHLPLEYNSALYSR_ CO6_HUMAN 7.17 621.0_538.3 LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN 6.06 972.0_798.4 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 3.23 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 2.8 QALEEFQK_496.8_680.3 CO8B_HUMAN 2.73 NIQSVNVK_451.3_674.4 GROA_HUMAN 2.53 DALSSVQESQVAQQAR_ APOC3_HUMAN 2.51 573.0_672.4 AVLHIGEK_289.5_348.7 THBG_HUMAN 2.33 FLNWIK_410.7_560.3 HABP2_HUMAN 1.05 FLPCENK_454.2_550.2 IL10_HUMAN 0.74 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 0.7 DISEVVTPR_508.3_787.4 CFAB_HUMAN 0.45 EVFSKPISWEELLQ_852.9_260.2 FA40A_HUMAN 0.17 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 0.06 TASDFITK_441.7_781.4 GELS_HUMAN −7.65

TABLE-US-00020 TABLE 19 Lasso All Early Window Coef- Variable Protein ficient FLNWIK_410.7_560.3 HABP2_HUMAN 3.74 AHYDLR_387.7_288.2 FETUA_HUMAN 0.07 ALNHLPLEYNSALYSR_ CO6_HUMAN 6.07 621.0_538.3 LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN 8.85 972.0_798.4 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 2.97 VQTAHFK_277.5_431.2 CO8A_HUMAN 3.36 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 11.24 VSEADSSNADWVTK_754.9_347.2 CFAB_HUMAN 0.63 AVLHIGEK_289.5_292.2 THBG_HUMAN 0.51 TGVAVNKPAEFTVDAK_ FLNA_HUMAN 0.17 549.6_977.5 LIENGYFHPVK_439.6_343.2 F13B_HUMAN 1.7 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN −0.93 758.0_574.3 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 1.4 TASDFITK_441.7_781.4 GELS_HUMAN −0.07 NIQSVNVK_451.3_674.4 GROA_HUMAN 2.12 DALSSVQESQVAQQAR_ APOC3_HUMAN 1.15 573.0_672.4 DALSSVQESQVAQQAR_ APOC3_HUMAN 0.09 573.0_502.3 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 2.45 ALDLSLK_380.2_575.3 ITIH3_HUMAN 2.51 TLFIFGVTK_513.3_811.5 PSG4_HUMAN 4.12 ISQGEADINIAFYQR_575.6_684.4 MMP8_HUMAN 1.29 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 0.55 GPGEDFR_389.2_322.2 PTGDS_HUMAN 0.07 DPNGLPPEAQK_583.3_669.4 RET4_HUMAN 1.36 WNFAYWAAHQPWSR_607.3_545.3 PRG2_HUMAN −1.27 ELCLDPK_437.7_359.2 IL8_HUMAN 0.3 FFQYDTWK_567.8_840.4 IGF2_HUMAN 1.83 IIEVEEEQEDPYLNDR_ FBLN1_HUMAN 1.14 996.0_777.4 ECEELEEK_533.2_405.2 IL15_HUMAN 1.78 LEEHYELR_363.5_580.3 PAI2_HUMAN 0.15 LNIGYIEDLK_589.3_837.4 PAI2_HUMAN 0.32 TAVTANLDIR_537.3_288.2 CHL1_HUMAN −0.98 SWNEPLYHLVTEVR_581.6_716.4 PRL_HUMAN 1.88 ILNIFGVIK_508.8_790.5 TFR1_HUMAN 0.05 TPSAAYLWVGTGASEAEK_ GELS_HUMAN −2.69 919.5_849.4 VGVISFAQK_474.8_693.4 TFR2_HUMAN −5.68 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN −1.43 GQVPENEANVVITTLK_571.3_462.3 CADH1_HUMAN −0.55 STPSLTTK_417.7_549.3 IL6RA_HUMAN −0.59 ALLLGWVPTR_563.3_373.2 PAR4_HUMAN −0.97

TABLE-US-00021 TABLE 20 Lasso SummedCoef Early Window SumBest Transition Protein Coefs LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN 1173.723955 972.0_798.4 ALNHLPLEYNSALYSR_ CO6_HUMAN 811.0150364 621.0_538.3 ELIEELVNITQNQK_ IL13_HUMAN 621.9659363 557.6_618.3 VQTAHFK_277.5_431.2 CO8A_HUMAN 454.178544 NIQSVNVK_451.3_674.4 GROA_HUMAN 355.9550674 TLFIFGVTK_ PSG4_HUMAN 331.8629189 513.3_811.5 GPGEDFR_389.2_322.2 PTGDS_HUMAN 305.9079494 FLPCENK_454.2_550.2 IL10_HUMAN 296.9473975 FLNWIK_410.7_560.3 HABP2_HUMAN 282.9841332 LIENGYFHPVK_ F13B_HUMAN 237.5320227 439.6_627.4 ECEELEEK_533.2_405.2 IL15_HUMAN 200.38281 FGFGGSTDSGPIR_ ADA12_HUMAN 194.6252869 649.3_745.4 QALEEFQK_496.8_680.3 CO8B_HUMAN 179.2518843 IIEVEEEQEDPYLNDR_ FBLN1_HUMAN 177.7534111 996.0_777.4 TYLHTYESEI_ ENPP2_HUMAN 164.9735228 628.3_908.4 ELIEELVNITQNQK_ IL13_HUMAN 162.2414693 557.6_517.3 LEEHYELR_363.5_580.3 PAI2_HUMAN 152.9262386 ISQGEADINIAFYQR_ MMP8_HUMAN 144.2445011 575.6_684.4 HPWIVHWDQLPQYQLNR_ KS6A3_HUMAN 140.2287926 744.0_918.5 AHYDLR_387.7_288.2 FETUA_HUMAN 137.9737525 GFQALGDAADIR_ TIMP1_HUMAN 130.4945567 617.3_288.2 SWNEPLYHLVTEVR_ PRL_HUMAN 127.442646 581.6_716.4 SGVDLADSNQK_ VGFR3_HUMAN 120.5149446 567.3_591.3 YENYTSSFFIR_ IL12B_HUMAN 117.0947487 713.8_293.1 FFQYDTWK_567.8_840.4 IGF2_HUMAN 109.8569617 HYFIAAVER_ FA8_HUMAN 106.9426543 553.3_658.4 ITGFLKPGK_ LBP_HUMAN 103.8056505 320.9_429.3 DALSSVQESQVAQQAR_ APOC3_HUMAN 98.50490812 573.0_502.3 SGVDLADSNQK_ VGFR3_HUMAN 97.19989285 567.3_662.3 ALDLSLK_380.2_575.3 ITIH3_HUMAN 94.84900337 TGVAVNKPAEFTVDAK_ FLNA_HUMAN 92.52335783 549.6_258.1 HPWIVHWDQLPQYQLNR_ KS6A3_HUMAN 91.77547608 744.0_1047.0 LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN 83.6483639 972.0_640.4 LNIGYIEDLK_ PAI2_HUMAN 83.50221521 589.3_837.4 IALGGLLFPASNLR_ SHBG_HUMAN 79.33146741 481.3_657.4 LPATEKPVLLSK_ HYOU1_HUMAN 78.89429168 432.6_460.3 FQLSETNR_497.8_605.3 PSG2_HUMAN 78.13445824 NEIVFPAGILQAPFYTR_ ECE1_HUMAN 75.12145257 968.5_357.2 ALDLSLK_380.2_185.1 ITIH3_HUMAN 63.05454715 DLHLSDVFLK_ CO6_HUMAN 58.26831142 396.2_366.2 TQILEWAAER_ EGLN_HUMAN 57.29461621 608.8_761.4 FSVVYAK_407.2_381.2 FETUA_HUMAN 54.78436389 VSEADSSNADWVTK_ CFAB_HUMAN 54.40003244 754.9_347.2 DPNGLPPEAQK_ RET4_HUMAN 53.89169348 583.3_669.4 VQEAHLTEDQIFYFPK_ CO8G_HUMAN 53.33747599 655.7_701.4 LSSPAVITDK_ PLMN_HUMAN 53.22513181 515.8_830.5 ITLPDFTGDLR_ LBP_HUMAN 51.5477235 624.3_288.2 AVLHIGEK_ THBG_HUMAN 49.73092632 289.5_292.2 GEVTYTTSQVSK_ EGLN_HUMAN 45.14743629 650.3_750.4 GYVIIKPLVWV_ SAMP_HUMAN 44.05164273 643.9_854.6 TGVAVNKPAEFTVDAK_ FLNA_HUMAN 42.99898046 549.6_977.5 YYGYTGAFR_ TRFL_HUMAN 42.90897411 549.3_450.3 ILDGGNK_358.7_490.2 CXCL5_HUMAN 42.60771281 FLPCENK_454.2_390.2 IL10_HUMAN 42.56799651 GFQALGDAADIR_ TIMP1_HUMAN 38.68456017 617.3_717.4 SDGAKPGPR_ COLI_HUMAN 38.47800265 442.7_213.6 NTGVISVVTTGLDR_ CADH1_HUMAN 32.62953675 716.4_662.4 SERPPIFEIR_ LRP1_HUMAN 31.48248968 415.2_288.2 DFHINLFQVLPWLK_ CFAB_HUMAN 31.27286268 885.5_400.2 DALSSVQESQVAQQAR_ APOC3_HUMAN 31.26972354 573.0_672.4 ELCLDPK_ IL8_HUMAN 29.91108737 437.7_359.2 ILNIFGVIK_ TFR1_HUMAN 29.88784921 508.8_790.5 TEFLSNYLTNVDDITLVPGT ENPP2_HUMAN 29.42327998 LGR_846.8_600.3 GAVHVVVAETDYQSFAVLYL CO8G_HUMAN 26.70286929 ER_822.8_863.5 AVLHIGEK_289.5_348.7 THBG_HUMAN 25.78703299 TFLTVYWTPER_ ICAM1_HUMAN 24.73090242 706.9_401.2 AGITIPR_364.2_486.3 IL17_HUMAN 23.84580477 GAVHVVVAETDYQSFAVLYL CO8G_HUMAN 23.81167843 ER_822.8_580.3 SLQAFVAVAAR_ IL23A_HUMAN 23.61468839 566.8_487.3 SWNEPLYHLVTEVR_ PRL_HUMAN 23.2538221 581.6_614.3 TYLHTYESEI_ ENPP2_HUMAN 22.70115313 628.3_515.3 TAHISGLPPSTDFIVYLSGL TENA_HUMAN 22.42695892 APSIR_871.5_800.5 QNYHQDSEAAINR_ FRIH_HUMAN 21.96827269 515.9_544.3 AHQLAIDTYQEFEETYIPK_ CSH_HUMAN 21.75765717 766.0_634.4 GDTYPAELYITGSILR_ F13B_HUMAN 20.89751398 885.0_274.1 AHYDLR_387.7_566.3 FETUA_HUMAN 20.67629529 IALGGLLFPASNLR_ SHBG_HUMAN 19.28973033 481.3_412.3 ATNATLDPR_ PAR1_HUMAN 18.77604574 479.8_272.2 FSVVYAK_407.2_579.4 FETUA_HUMAN 17.81136564 HTLNQIDEVK_ FETUA_HUMAN 17.29763288 598.8_951.5 DIPHWLNPTR_ PAPP1_HUMAN 17.00562521 416.9_373.2 LYYGDDEK_ CO8A_HUMAN 16.78897272 501.7_563.2 AALAAFNAQNNGSNFQLEE FETUA_HUMAN 16.41986569 ISR_789.1_633.3 IQTHSTTYR_ F13B_HUMAN 15.78335174 369.5_627.3 GPITSAAELNDPQSILLR_ EGLN_HUMAN 15.3936876 632.4_826.5 QTLSWTVTPK_ PZP_HUMAN 14.92509259 580.8_818.4 AVGYLITGYQR_ PZP_HUMAN 13.9795325 620.8_737.4 DIIKPDPPK_ IL12B_HUMAN 13.76508282 511.8_342.2 YNCILLR_403.7_288.2 ENOA_HUMAN 12.61733711 GNGLTWAEK_ C163B_HUMAN 12.5891421 488.3_634.3 QVFAVQR_424.2_473.3 ELNE_HUMAN 12.57709327 FLQEQGHR_ CO8G_HUMAN 12.51843475 338.8_497.3 HVVQLR_376.2_515.3 IL6RA_HUMAN 11.83747559 DVLLLVHNLPQNLTGHIW PSG7_HUMAN 11.69074708 YK_791.8_883.0 TFLTVYWTPER_ ICAM1_HUMAN 11.63709776 706.9_502.3 VELAPLPSWQPVGK_ ICAM1_HUMAN 10.79897269 760.9_400.3 TLFIFGVTK_ PSG4_HUMAN 10.2831751 513.3_215.1 AYSDLSR_406.2_375.2 SAMP_HUMAN 10.00461148 HATLSLSIPR_ VGFR3_HUMAN 9.967933028 365.6_472.3 LQGTLPVEAR_ CO5_HUMAN 9.963760572 542.3_571.3 NTVISVNPSTK_ VCAM1_HUMAN 9.124228658 580.3_732.4 EVFSKPISWEELLQ_ FA40A-HUMAN 8.527980294 852.9_260.2 SLCINASAIESILK_ IL3_HUMAN 8.429061621 687.4_860.5 IQHPFTVEEFVLPK_ PZP_HUMAN 7.996504258 562.0_861.5 GVTGYFTFNLYLK_ PSG5_HUMAN 7.94396229 508.3_683.9 VFQYIDLHQDEFVQTLK_ CNDP1_HUMAN 7.860590049 708.4_361.2 ILDDLSPR_464.8_587.3 ITIH4_HUMAN 7.593889262 LIENGYFHPVK_ F13B_HUMAN 7.05838337 439.6_343.2 VFQFLEK_455.8_811.4 CO5_HUMAN 6.976884759 AFTECCVVASQLR_ CO5_HUMAN 6.847474286 770.9_574.3 WWGGQPLWITATK_ ENPP2_HUMAN 6.744837357 772.4_929.5 IQTHSTTYR_ F13B_HUMAN 6.71464509 369.5_540.3 IAQYYYTFK_ F13B_HUMAN 6.540497911 598.8_395.2 YGFYTHVFR_ THRB_HUMAN 6.326347548 397.2_421.3 YHFEALADTGISSEFYDNAN CO8A_HUMAN 6.261787525 DLLSK_940.8_874.5 ANDQYLTAAALHNLDEAVK_ IL1A_HUMAN 6.217191651 686.4_301.1 FSLVSGWGQLLDR_ FA7-HUMAN 6.1038295 493.3_403.2 GWVTDGFSSLK_ APOC3_HUMAN 6.053494609 598.8_854.4 TLEAQLTPR_514.8_814.4 HEP2_HUMAN 5.855967278 VSAPSGTGHLPGLNPL_ PSG3_HUMAN 5.625944609 506.3_300.7 EAQLPVIENK_ PLMN_HUMAN 5.407703773 570.8_699.4 SPEAEDPLGVER_ Z512B_HUMAN 5.341420139 649.8_670.4 IAIDLFK_410.3_635.4 HEP2_HUMAN 4.698739039 YEFLNGR_449.7_293.1 PLMN_HUMAN 4.658286706 VQTAHFK_277.5_502.3 CO8A_HUMAN 4.628247194 IEVIITLK_464.8_815.5 CXL11_HUMAN 4.57198762 ILTPEVR_414.3_601.3 GDF15_HUMAN 4.452884608 LEEHYELR_363.5_288.2 PAI2_HUMAN 4.411983862 HATLSLSIPR_ VGFR3_HUMAN 4.334242077 365.6_272.2 NSDQEIDFK_ S10A5_HUMAN 4.25302369 548.3_294.2 LPNNVLQEK_ AFAM-HUMAN 4.183602548 527.8_844.5 ELANTIK_394.7_475.3 S10AC_HUMAN 4.13558153 LSIPQITTK_ PSG5_HUMAN 3.966238797 500.8_687.4 TLNAYDHR_ PAR3_HUMAN 3.961140111 330.5_312.2 WWGGQPLWITATK_ ENPP2_HUMAN 3.941476057 772.4_373.2 ELLESYIDGR_ THRB_HUMAN 3.832723338 597.8_710.4 ATLSAAPSNPR_ CXCL2_HUMAN 3.82834767 542.8_570.3 VVLSSGSGPGLDLPLVLGLP SHBG_HUMAN 3.80737887 LQLK_791.5_598.4 NADYSYSVWK_ CO5_HUMAN 3.56404167 616.8_333.2 ILILPSVTR_ PSGx_HUMAN 3.526998593 506.3_559.3 ALEQDLPVNIK_ CNDP1_HUMAN 3.410412424 620.4_798.5 QVCADPSEEWVQK_ CCL3_HUMAN 3.30795151 788.4_275.2 SVCINDSQAIAEVLNQLK_ DESP_HUMAN 3.259270741 619.7_914.5 QVFAVQR_424.2_620.4 ELNE_HUMAN 3.211482663 ALPGEQQPLHALTR_ IBP1_HUMAN 3.211207158 511.0_807.5 LEPLYSASGPGLRPLVIK_ CAA60698 3.203088951 637.4_260.2 GTYLYNDCPGPGQDTDCR_ TNR1A_HUMAN 3.139418139 697.0_666.3 DAGLSWGSAR_ NEUR4_HUMAN 3.005197927 510.2_576.3 YGFYTHVFR_ THRB_HUMAN 2.985663918 397.2_659.4 NNQLVAGYLQGPNVNLEEK_ IL1RA_HUMAN 2.866983196 700.7_357.2 EKPAGGIPVLGSLVNTVLK_ BPIB1_HUMAN 2.798965142 631.4_930.6 FGSDDEGR_441.7_735.3 PTHR_HUMAN 2.743283546 IEVNESGTVASSSTAVIVSAR_ PAI1_HUMAN 2.699725572 693.0_545.3 FATTFYQHLADSK_ ANT3_HUMAN 2.615073729 510.3_533.3 DYWSTVK_449.7_347.2 APOC3_HUMAN 2.525459346 QLGLPGPPDVPDHAAYHPF_ ITIH4_HUMAN 2.525383799 676.7_263.1 LSSPAVITDK_ PLMN_HUMAN 2.522306831 515.8_743.4 TEFLSNYLTNVDDITLVPG ENPP2_HUMAN 2.473366805 TLGR_846.8_699.4 SILFLGK_389.2_201.1 THBG_HUMAN 2.472413913 VTFEYR_407.7_614.3 CRHBP_HUMAN 2.425338167 SVVLIPLGAVDDGEHSCIN CNDP1_HUMAN 2.421340244 EK_703.0_798.4 HTLNQIDEVK_ FETUA_HUMAN 2.419851187 598.8_958.5 ALNSIIDVYHK_ S10A8_HUMAN 2.367904596 424.9_661.3 ETLALLSTHR_ IL5_HUMAN 2.230076769 570.8_500.3 GLQYAAQEGLLALQSELLR_ LBP_HUMAN 2.205949216 1037.1_858.5 TYNVDK_370.2_262.1 PPB1_HUMAN 2.11849772 FTITAGSK_412.7_576.3 FABPL_HUMAN 2.098589805 GIVEECCFR_ IGF2_HUMAN 2.059942995 585.3_900.3 YGIEEHGK_ CXA1_HUMAN 2.033828589 311.5_599.3 ALVLELAK_ INHBE_HUMAN 1.993820617 428.8_331.2 ITLPDFTGDLR_ LBP_HUMAN 1.968753183 624.3_920.5 HELTDEELQSLFTNFANVV AFAM_HUMAN 1.916438806 DK_817.1_906.5 EANQSTLENFLER_ IL4_HUMAN 1.902033355 775.9_678.4 DADPDTFFAK_ AFAM_HUMAN 1.882254674 563.8_825.4 LFIPQITR_ PSG9_HUMAN 1.860649392 494.3_727.4 DPNGLPPEAQK_ RET4_HUMAN 1.847702127 583.3_497.2 VEPLYELVTATDFAYSSTV CO8B_HUMAN 1.842159131 R_754.4_549.3 FQLSETNR_497.8_476.3 PSG2_HUMAN 1.834693717 FSLVSGWGQLLDR_ FA7_HUMAN 1.790582748 493.3_516.3 NKPGVYTDVAYYLAWIR_ FA12_HUMAN 1.777303353 677.0_545.3 FTGSQPFGQGVEHATANK_ TSP1_HUMAN 1.736517431 626.0_521.2 DDLYVSDAFHK_ ANT3_HUMAN 1.717534082 655.3_704.3 AFLEVNEEGSEAAASTAVVI ANT3_HUMAN 1.679420475 AGR_764.4_685.4 LPNNVLQEK_ AFAM_HUMAN 1.66321148 527.8_730.4 IVLSLDVPIGLLQILLEQA UCN2_HUMAN 1.644983604 R_735.1_503.3 DPTFIPAPIQAK_ ANGT_HUMAN 1.625411496 433.2_556.3 SDLEVAHYK_ CO8B_HUMAN 1.543640117 531.3_617.3 QLYGDTGVLGR_ CO8G_HUMAN 1.505242962 589.8_501.3 VNHVTLSQPK_ B2MG_HUMAN 1.48233058 374.9_459.3 TLLPVSKPEIR_ CO5_HUMAN 1.439531341 418.3_288.2 SEYGAALAWEK_ CO6_HUMAN 1.424401638 612.8_845.5 YGIEEHGK_311.5_341.2 CXA1_HUMAN 1.379872204 DAGLSWGSAR_ NEUR4_HUMAN 1.334272677 510.3_390.2 AEHPTWGDEQLFQTTR_ PGH1_HUMAN 1.30549273 639.3_569.3 FQSVFTVTR_ C1QC_HUMAN 1.302847429 542.8_623.4 VPGLYYFTYHASSR_ C1QB_HUMAN 1.245565877 554.3_420.2 AYSDLSR_406.2_577.3 SAMP_HUMAN 1.220777002 ALEQDLPVNIK_ CNDP1_HUMAN 1.216612522 620.4_570.4 NAVVQGLEQPHGLVVHPLR_ LRP1_HUMAN 1.212935735 688.4_890.6 TSDQIHFFFAK_ ANT3_HUMAN 1.176238265 447.6_659.4 GTYLYNDCPGPGQDTDCR_ TNR1A_HUMAN 1.1455649 697.0_335.2 TSYQVYSK_488.2_787.4 C163A_HUMAN 1.048896429 ALNSIIDVYHK_ S10A8_HUMAN 1.028522516 424.9_774.4 VELAPLPSWQPVGK_ ICAM1_HUMAN 0.995831393 760.9_342.2 LSETNR_360.2_330.2 PSG1_HUMAN 0.976094717 HFQNLGK_422.2_527.2 AFAM_HUMAN 0.956286531 ELPQSIVYK_ FBLN3_HUMAN 0.947931674 538.8_417.7 LPATEKPVLLSK_ HYOU1_HUMAN 0.932537153 432.6_347.2 SPEAEDPLGVER_ Z512B_HUMAN 0.905955419 649.8_314.1 DEIPHNDIALLK_ HABP2_HUMAN 0.9032484 459.9_510.8 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.884340285 LIEIANHVDK_ ADA12_HUMAN 0.881493383 384.6_498.3 AGFAGDDAPR_ ACTB_HUMAN 0.814836556 488.7_701.3 YEFLNGR_449.7_606.3 PLMN_HUMAN 0.767373087 VIAVNEVGR_ CHL1_HUMAN 0.721519592 478.8_284.2 SLSQQIENIR_ CO1A1_HUMAN 0.712051082 594.3_531.3 EWVAIESDSVQPVPR_ CNDP1_HUMAN 0.647712421 856.4_486.2 YGLVTYATYPK_ CFAB_HUMAN 0.618499569 638.3_843.4 SVVLIPLGAVDDGEHSCINE CNDP1_HUMAN 0.606626346 K_703.0_286.2 NSDQEIDFK_ S10A5_HUMAN 0.601928175 548.3_409.2 NVNQSLLELHK_ FRIH_HUMAN 0.572008792 432.2_543.3 IAQYYYTFK_5 F13B_HUMAN 0.495062844 98.8_884.4 GPITSAAELNDPQSILLR_ EGLN_HUMAN 0.47565795 632.4_601.4 YTTEIIK_434.2_704.4 C1R_HUMAN 0.433318952 GYVIIKPLVWV_ SAMP_HUMAN 0.427905264 643.9_304.2 LDFHFSSDR_ INHBC_HUMAN 0.411898116 375.2_464.2 IPSNPSHR_ FBLN3_HUMAN 0.390037291 303.2_496.3 APLTKPLK_ CRP_HUMAN 0.38859469 289.9_357.2 EVFSKPISWEELLQ_ FA40A_HUMAN 0.371359974 852.9_376.2 YENYTSSFFIR_ IL12B_HUMAN 0.346336267 713.8_756.4 SPQAFYR_434.7_556.3 REL3_HUMAN 0.345901234 SVDEALR_395.2_488.3 PRDX2_HUMAN 0.307518869 FVFGTTPEDILR_ TSP1_HUMAN 0.302313589 697.9_742.4 FTFTLHLETPKPSISSSNLN PSG1_HUMAN 0.269826678 PR_829.4_787.4 VGEYSLYIGR_ SAMP_HUMAN 0.226573173 578.8_708.4 ILPSVPK_377.2_244.2 PGH1_HUMAN 0.225429414 LFIPQITR_494.3_614.4 PSG9_HUMAN 0.18285533 TGYYFDGISR_ FBLN1_HUMAN 0.182474114 589.8_857.4 HYGGLTGLNK_ PGAM1_HUMAN 0.152397007 530.3_759.4 NQSPVLEPVGR_ KS6A3_HUMAN 0.128963949 598.3_866.5 IGKPAPDFK_ PRDX2_HUMAN 0.113383235 324.9_294.2 TSESTGSLPSPFLR_ PSMG1_HUMAN 0.108159874 739.9_716.4 ESDTSYVSLK_ CRP_HUMAN 0.08569303 564.8_347.2 ETPEGAEAKPWYEPIYLGGV TNFA_HUMAN 0.039781728 FQLEK_951.1_877.5 TSDQIHFFFAK_ ANT3_HUMAN 0.008064465 447.6_512.3

TABLE-US-00022 TABLE 21 Lasso32 Middle Window Coef- Variable UniProt_ID ficient SEYGAALAWEK_612.8_788.4 CO6_HUMAN 6.99 VFQFLEK_455.8_811.4 CO5_HUMAN 6.43 VLEPTLK_400.3_458.3 VTDB_HUMAN 3.99 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 3.33 TLAFVR_353.7_492.3 FA7_HUMAN 2.44 YGIEEHGK_311.5_599.3 CXA1_HUMAN 2.27 LHEAFSPVSYQHDLALLR_ FA12_HUMAN 2.14 699.4_251.2 QGHNSVFLIK_381.6_520.4 HEMO_HUMAN 0.25 LLAPSDSPEWLSFDVTGVVR_ TGFB1_HUMAN −2.81 730.1_430.3 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −3.46 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −6.61

TABLE-US-00023 TABLE 22 Lasso100 Middle Window Coef- Variable UniProt_ID ficient VFQFLEK_455.8_811.4 CO5_HUMAN 6.89 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 4.67 GEVTYTTSQVSK_650.3_750.4 EGLN_HUMAN 3.4 QVFAVQR_424.2_473.3 ELNE_HUMAN 1.94 VELAPLPSWQPVGK_760.9_342.2 ICAM1_HUMAN 1.91 LHEAFSPVSYQHDLALLR_ FA12_HUMAN 1.8 699.4_251.2 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 1.67 YGIEEHGK_311.5_599.3 CXA1_HUMAN 1.53 YGIEEHGK_311.5_341.2 CXA1_HUMAN 1.51 HYINLITR_515.3_301.1 NPY_HUMAN 1.47 TLAFVR_353.7_492.3 FA7_HUMAN 1.46 GVTGYFTFNLYLK_508.3_260.2 PSG5_HUMAN 1.28 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 0.84 DALSSVQESQVAQQAR_ APOC3_HUMAN 0.41 573.0_502.3 VELAPLPSWQPVGK_760.9_400.3 ICAM1_HUMAN 0.3 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN −0.95 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −1.54 DVLLLVHNLPQNLTGHIWYK_ PSG7_HUMAN −1.54 791.8_310.2 VPLALFALNR_557.3_620.4 PEPD_HUMAN −1.91 LLAPSDSPEWLSFDVTGVVR_ TGFB1_HUMAN −2.3 730.1_430.3 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −3.6 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN −3.96

TABLE-US-00024 TABLE 23 Lasso Protein Middle Window Coef- Variable UniProt_ID ficient SEYGAALAWEK_612.8_788.4 CO6_HUMAN 5.84 VFQFLEK_455.8_811.4 CO5_HUMAN 5.58 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 2.11 TLAFVR_353.7_492.3 FA7_HUMAN 1.83 LHEAFSPVSYQHDLALLR_ FA12_HUMAN 1.62 699.4_251.2 HYINLITR_515.3_301.1 NPY_HUMAN 1.39 VLEPTLK_400.3_458.3 VTDB_HUMAN 1.37 YGIEEHGK_311.5_599.3 CXA1_HUMAN 1.17 VELAPLPSWQPVGK_ ICAM1_HUMAN 1.13 760.9_342.2 QVFAVQR_424.2_473.3 ELNE_HUMAN 0.79 ANLINNIFELAGLGK_ LCAP_HUMAN 0.23 793.9_299.2 DVLLLVHNLPQNLTGHIWYK_ PSG7_HUMAN −0.61 791.8_310.2 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN −0.69 AVDIPGLEAATPYR_ TENA_HUMAN −0.85 736.9_399.2 VPLALFALNR_557.3_620.4 PEPD_HUMAN −1.45 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −1.9 LLAPSDSPEWLSFDVTGVVR_ TGFB1_HUMAN −2.07 730.1_430.3 EVFSKPISWEELLQ_ FA40A_HUMAN −2.32 852.9_376.2

TABLE-US-00025 TABLE 24 Lasso All Middle Window Coef- Variable UniProt_ID ficient SEYGAALAWEK_612.8_788.4 CO6_HUMAN 2.48 VFQFLEK_455.8_811.4 CO5_HUMAN 2.41 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN 1.07 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.64 VLEPTLK_400.3_458.3 VTDB_HUMAN 0.58 LHEAFSPVSYQHDLALLR_ FA12_HUMAN 0.21 699.4_251.2 LLAPSDSPEWLSFDVTGVVR_ TGFB1_HUMAN −0.62 730.1_430.3 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −1.28

TABLE-US-00026 TABLE 25 Lasso32 Middle-Late Window Coef- Variable UniProt_ID ficient SEYGAALAWEK_612.8_845.5 CO6_HUMAN 4.35 TLAFVR_353.7_492.3 FA7_HUMAN 2.42 YGIEEHGK_311.5_599.3 CXA1_HUMAN 1.46 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 1.37 VFQFLEK_455.8_811.4 CO5_HUMAN 0.89 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.85 QINSYVK_426.2_496.3 CBG_HUMAN 0.56 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.53 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 0.39 TEQAAVAR_423.2_615.4 FA12_HUMAN 0.26 VLEPTLK_400.3_587.3 VTDB_HUMAN 0.24 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN −2.08 758.0_574.3 VPLALFALNR_557.3_620.4 PEPD_HUMAN −2.09 AVYEAVLR_460.8_587.4 PEPD_HUMAN −3.37

TABLE-US-00027 TABLE 26 Lasso100 Middle-Late Window Variable UniProt_ID Coefficient VFQFLEK_455.8_811.4 CO5_HUMAN 3.82 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 2.94 YGIEEHGK_311.5_599.3 CXA1_HUMAN 2.39 DPTFIPAPIQAK_433.2_556.3 ANGT_HUMAN 2.05 TLAFVR_353.7_492.3 FA7_HUMAN 1.9 NQSPVLEPVGR_598.3_866.5 KS6A3_HUMAN 1.87 ALNHLPLEYNSALYSR_621.0_ CO6_HUMAN 1.4 538.3 TQILEWAAER_608.8_761.4 EGLN_HUMAN 1.29 VVGGLVALR_442.3_784.5 FA12_HUMAN 1.24 QINSYVK_426.2_496.3 CBG_HUMAN 1.14 YGIEEHGK_311.5_341.2 CXA1_HUMAN 0.84 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.74 GTYLYNDCPGPGQDTDCR_697.0_ TNR1A_HUMAN 0.51 666.3 SLCINASAIESILK_687.4_860.5 IL3_HUMAN 0.44 DLHLSDVFLK_396.2_260.2 CO6_HUMAN 0.38 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.37 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.3 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.19 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.19 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.15 AALAAFNAQNNGSNFQLEEISR_ FETUA_HUMAN −0.09 789.1_746.4 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN −0.52 758.0_574.3 TSYQVYSK_488.2_787.4 C163A_HUMAN −0.62 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN −1.29 TAHISGLPPSTDFIVYLSGLAPSIR_ TENA_HUMAN −1.53 871.5_472.3 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −1.73 LLAPSDSPEWLSFDVTGVVR_730.1_ TGFB1_HUMAN −1.95 430.3 VPLALFALNR_557.3_620.4 PEPD_HUMAN −2.9 AVYEAVLR_460.8_587.4 PEPD_HUMAN −3.04 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −3.49 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN −3.71

TABLE-US-00028 TABLE 27 Lasso Protein Middle-LateWindow Variable UniProt_ID Coefficient VFQFLEK_455.8_811.4 CO5_HUMAN 4.25 ALNHLPLEYNSALYSR_621.0_ CO6_HUMAN 3.06 696.4 YGIEEHGK_311.5_599.3 CXA1_HUMAN 2.36 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 2.11 TQILEWAAER_608.8_761.4 EGLN_HUMAN 1.81 NQSPVLEPVGR_598.3_866.5 KS6A3_HUMAN 1.79 TEQAAVAR_423.2_615.4 FA12_HUMAN 1.72 QINSYVK_426.2_496.3 CBG_HUMAN 0.98 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.98 NCSFSIIYPVVIK_770.4_555.4 CRHBP_HUMAN 0.76 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.63 SLCINASAIESILK_687.4_860.5 IL3_HUMAN 0.59 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN 0.55 GTYLYNDCPGPGQDTDCR_697.0_ TNR1A_HUMAN 0.55 666.3 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0.46 NIQSVNVK_451.3_674.4 GROA_HUMAN 0.22 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.11 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.01 TSYQVYSK_488.2_787.4 C163A_HUMAN −0.76 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN −1.31 758.0_574.3 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −1.59 LLAPSDSPEWLSFDVTGVVR_ TGFB1_HUMAN −1.73 730.1_430.3 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN −2.02 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN −3 TGVAVNKPAEFTVDAK_549.6_ FLNA_HUMAN −3.15 258.1 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN −3.49 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −3.82 VPLALFALNR_557.3_620.4 PEPD_HUMAN −4.94

TABLE-US-00029 TABLE 28 Lasso All Middle-LateWindow Variable UniProt_ID Coefficient ALNHLPLEYNSALYSR_621.0_ CO6_HUMAN 2.38 538.3 TLAFVR_353.7_492.3 FA7_HUMAN 0.96 YGIEEHGK_311.5_599.3 CXA1_HUMAN 0.34 DPTFIPAPIQAK_433.2_461.2 ANGT_HUMAN 0.33 DFNQFSSGEK_386.8_333.2 FETA_HUMAN 0.13 QINSYVK_426.2_496.3 CBG_HUMAN 0.03 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 0 AQPVQVAEGSEPDGFWEALGGK_ GELS_HUMAN −0.02 758.0_574.3 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −0.05 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN −0.12 LLAPSDSPEWLSFDVTGVVR_730._ TGFB1_HUMAN −0.17 1430.3 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN −0.31 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN −0.35 VPLALFALNR_557.3_620.4 PEPD_HUMAN −0.43 AVYEAVLR_460.8_587.4 PEPD_HUMAN −2.33

TABLE-US-00030 TABLE 29 Lasso 32 LateWindow Variable UniProt_ID Coefficient QINSYVK_426.2_610.3 CBG_HUMAN 3.24 ILDGGNK_358.7_603.3 CXCL5_HUMAN 2.65 VFQYIDLHQDEFVQTLK_708.4_ CNDP1_HUMAN 2.55 375.2 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 2.12 YSHYNER_323.5_418.2 HABP2_HUMAN 1.63 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 1.22 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 0.96 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 0.86 GTYLYNDCPGPGQDTDCR_697.0_ TNR1A_HUMAN 0.45 666.3 TSYQVYSK_488.2_787.4 C163A_HUMAN −1.73 TGVAVNKPAEFTVDAK_549.6_ FLNA_HUMAN −2.56 258.1 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN −3.04 VPLALFALNR_557.3_620.4 PEPD_HUMAN −3.33 YYGYTGAFR_549.3_450.3 TRFL_HUMAN −4.24 AVYEAVLR_460.8_587.4 PEPD_HUMAN −5.83 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −6.52 AALAAFNAQNNGSNFQLEEISR_ FETUA_HUMAN −6.55 789.1_746.4

TABLE-US-00031 TABLE 30 Lasso 100 Late Window Variable UniProt_ID Coefficient SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 4.13 ILDGGNK_358.7_603.3 CXCL5_HUMAN 3.57 QINSYVK_426.2_610.3 CBG_HUMAN 3.41 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 1.64 VFQYIDLHQDEFVQTLK_708.4_ CNDP1_HUMAN 1.57 375.2 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 1.45 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.71 YSHYNER_323.5_418.2 HABP2_HUMAN 0.68 FFQYDTWK_567.8_712.3 IGF2_HUMAN 0.42 IEVNESGTVASSSTAVIVSAR_ PAI1_HUMAN 0.36 693.0_545.3 GTYLYNDCPGPGQDTDCR_697.0_ TNR1A_HUMAN 0.21 666.3 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.1 VGVISFAQK_474.8_580.3 TFR2_HUMAN 0.08 TSYQVYSK_488.2_787.4 C163A_HUMAN −0.36 ALNFGGIGVVVGHELTHAFDDQGR_ ECE1_HUMAN −0.65 837.1_360.2 AYSDLSR_406.2_375.2 SAMP_HUMAN −1.23 TGVAVNKPAEFTVDAK_549.6_ FLNA_HUMAN −1.63 258.1 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN −2.29 YYGYTGAFR_549.3_450.3 TRFL_HUMAN −2.58 VPLALFALNR_557.3_620.4 PEPD_HUMAN −2.73 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN −2.87 AVDIPGLEAATPYR_736.9_286.1 TENA_HUMAN −3.9 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −5.29 AVYEAVLR_460.8_587.4 PEPD_HUMAN −5.51 AALAAFNACINNGSNFQLEEISR_ FETUA_HUMAN −6.49 789.1_746.4

TABLE-US-00032 TABLE 31 Lasso Protein Late Window Variable UniProt_ID Coefficient SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 3.33 ILDGGNK_358.7_603.3 CXCL5_HUMAN 3.25 QINSYVK_426.2_496.3 CBG_HUMAN 2.41 YSHYNER_323.5_418.2 HABP2_HUMAN 1.82 ALEQDLPVNIK_620.4_798.5 CNDP1_HUMAN 1.32 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 1.27 GTYLYNDCPGPGQDTDCR_ TNR1A_HUMAN 0.26 697.0_666.3 IEVNESGTVASSSTAVIVSAR_ PAI1_HUMAN 0.18 693.0_545.3 LTTVDIVTLR_565.8_815.5 IL2RB_HUMAN 0.18 TSYQVYSK_488.2_787.4 C163A_HUMAN −0.11 TGVAVNKPAEFTVDAK_549.6_ FLNA_HUMAN −0.89 258.1 AYSDLSR_406.2_375.2 SAMP_HUMAN −1.47 SPEAEDPLGVER_649.8_314.1 Z512B_HUMAN −1.79 YYGYTGAFR_549.3_450.3 TRFL_HUMAN −2.22 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN −2.41 AVDIPGLEAATPYR_736.9_286.1 TENA_HUMAN −2.94 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −5.18 AALAAFNAQNNGSNFQLEEISR_ FETUA_HUMAN −5.71 789.1_746.4 AVYEAVLR_460.8_587.4 PEPD_HUMAN −7.33

TABLE-US-00033 TABLE 32 Lasso All Late Window Variable UniProt_ID Coefficient QINSYVK_426.2_496.3 CBG_HUMAN 0.5 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 0.15 ALEQDLPVNIK_620.4_570.4 CNDP1_HUMAN 0.11 ILDGGNK_358.7_603.3 CXCL5_HUMAN 0.08 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 0.06 YYGYTGAFR_549.3_450.3 TRFL_HUMAN −0.39 AALAAFNACINNGSNFQLEEISR_ FETUA_HUMAN −1.57 789.1_746.4 AEIEYLEK_497.8_552.3 LYAM1_HUMAN −2.46 AVYEAVLR_460.8_587.4 PEPD_HUMAN −2.92

TABLE-US-00034 TABLE 33 Random Forest 32 Early Window Variable Protein MeanDecreaseGini ELIEELVNITQNQK_557.6_ IL13_HUMAN  3.224369171 517.3 AHYDLR_387.7_288.2 FETUA_HUMAN  1.869007658 FSVVYAK_407.2_381.2 FETUA_HUMAN  1.770198171 ITLPDFTGDLR_624.3_ LBP_HUMAN  1.710936472 288.2 ITGFLKPGK_320.9_301.2 LBP_HUMAN  1.623922439 ITGFLKPGK_320.9_429.3 LBP_HUMAN  1.408035272 ELIEELVNITQNQK_557.6_ IL13_HUMAN  1.345412168 618.3 VFQFLEK_455.8_811.4 CO5_HUMAN  1.311332013 VQTAHFK_277.5_431.2 CO8A_HUMAN  1.308902373 FLNWIK_410.7_560.3 HABP2_HUMAN  1.308093745 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN  1.297033607 TLLPVSKPEIR_418.3_ CO5_HUMAN  1.291280928 288.2 LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN 1.28622301 972.0_798.4 QALEEFQK_496.8_680.3 CO8B_HUMAN  1.191731825 FSVVYAK_407.2_579.4 FETUA_HUMAN  1.078909138 ITLPDFTGDLR_624.3_ LBP_HUMAN  1.072613747 920.5 AHYDLR_387.7_566.3 FETUA_HUMAN  1.029562263 ALNHLPLEYNSALYSR_ CO6_HUMAN 1.00992071 621.0_538.3 DVLLLVHNLPQNLPGYFWYK_ PSG9_HUMAN  1.007095529 810.4_967.5 SFRPFVPR_335.9_635.3 LBP_HUMAN  0.970312536 SDLEVAHYK_531.3_617.3 CO8B_HUMAN  0.967904893 VQEAHLTEDQIFYFPK_ CO8G_HUMAN  0.960398254 655.7_701.4 VFQFLEK_455.8_276.2 CO5_HUMAN  0.931652095 SLLQPNK_400.2_599.4 CO8A_HUMAN  0.926470249 SFRPFVPR_335.9_272.2 LBP_HUMAN  0.911599611 FLNWIK_410.7_561.3 HABP2_HUMAN  0.852022868 LSSPAVITDK_515.8_743.4 PLMN_HUMAN  0.825455824 DVLLLVHNLPQNLPGYFWYK_ PSG9_HUMAN  0.756797142 810.4_594.3 ALVLELAK_428.8_672.4 INHBE_HUMAN  0.748802555 DISEVVTPR_508.3_787.4 CFAB_HUMAN  0.733731518

TABLE-US-00035 TABLE 34 Random Forest 100 Early Window Variable Protein MeanDecreaseGini ELIEELVNITQNQK_557.6_ IL13_HUMAN   1.709778508 517.3 LPNNVLQEK_527.8_844.5 AFAM_HUMAN   0.961692716 AHYDLR_387.7_288.2 FETUA_HUMAN   0.901586746 ITLPDFTGDLR_624.3_ LBP_HUMAN   0.879119498 288.2 IEGNLIFDPNNYLPK_874.0_ APOB_HUMAN   0.842483095 414.2 ITGFLKPGK_320.9_301.2 LBP_HUMAN   0.806905233 FSVVYAK_407.2_381.2 FETUA_HUMAN   0.790429706 ITGFLKPGK_320.9_429.3 LBP_HUMAN   0.710312386 VFQFLEK_455.8_811.4 CO5_HUMAN   0.709531553 LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN   0.624325189 972.0_798.4 DADPDTFFAK_563.8_825.4 AFAM_HUMAN   0.618684313 FLNWIK_410.7_560.3 HABP2_HUMAN   0.617501242 TASDFITK_441.7_781.4 GELS_HUMAN   0.609275999 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN   0.588718595 VQTAHFK_277.5_431.2 CO8A_HUMAN  0.58669845 TLLPVSKPEIR_418.3_ COS_HUMAN 0.5670608 288.2 ELIEELVNITQNQK_557.6_ IL13_HUMAN   0.555624783 618.3 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN   0.537678415 HFQNLGK_422.2_527.2 AFAM_HUMAN   0.535543137 TASDFITK_441.7_710.4 GELS_HUMAN   0.532743323 ITLPDFTGDLR_624.3_ LBP_HUMAN  0.51667902 920.5 QALEEFQK_496.8_680.3 CO8B_HUMAN   0.511314017 AVLHIGEK_289.5_348.7 THBG_HUMAN   0.510284122 FSVVYAK_407.2_579.4 FETUA_HUMAN   0.503907813 LPNNVLQEK_527.8_730.4 AFAM_HUMAN   0.501281631 AHYDLR_387.7_566.3 FETUA_HUMAN   0.474166711 IAPQLSTEELVSLGEK_ AFAM_HUMAN   0.459595701 857.5_333.2 WWGGQPLWITATK_772.4_ ENPP2_HUMAN  0.44680777 929.5 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN   0.434157773 DALSSVQESQVAQQAR_ APOC3_HUMAN   0.432484862 573.0_502.3

TABLE-US-00036 TABLE 35 Random Forest Protein Early Window Variable Protein MeanDecreaseGini ELIEELVNITQNQK_557.6_ IL13_HUMAN  2.881452809 517.3 LPNNVLQEK_527.8_844.5 AFAM_HUMAN  1.833987752 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN  1.608843881 IEGNLIFDPNNYLPK_874.0_ APOB_HUMAN  1.594658208 414.2 VFQFLEK_455.8_811.4 CO5_HUMAN  1.290134412 LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN  1.167981736 972.0_798.4 TASDFITK_441.7_781.4 GELS_HUMAN  1.152847453 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN  1.146752656 FSVVYAK_407.2_579.4 FETUA_HUMAN  1.060168583 AVLHIGEK_289.5_348.7 THBG_HUMAN  1.033625773 FLNWIK_410.7_560.3 HABP2_HUMAN  1.022356789 QALEEFQK_496.8_680.3 CO8B_HUMAN  0.990074129 DVLLLVHNLPQNLPGYFWYK_ PSG9_HUMAN  0.929633865 810.4_967.5 WWGGQPLWITATK_772.4_ ENPP2_HUMAN  0.905895642 929.5 VQEAHLTEDQIFYFPK_ CO8G_HUMAN  0.883887371 655.7_701.4 NNQLVAGYLQGPNVNLEEK_ IL1RA_HUMAN  0.806472085 700.7_999.5 SLLQPNK_400.2_599.4 CO8A_HUMAN  0.783623222 DALSSVQESQVAQQAR_ APOC3_HUMAN  0.774365756 573.0_672.4 NIQSVNVK_451.3_674.4 GROA_HUMAN  0.767963386 HPWIVHWDQLPQYQLNR_ KS6A3_HUMAN  0.759960139 744.0_1047.0 TTSDGGYSFK_531.7_860.4 INHA_HUMAN  0.732813448 ALNHLPLEYNSALYSR_ CO6_HUMAN  0.718779092 621.0_538.3 LSSPAVITDK_515.8_743.4 PLMN_HUMAN  0.699547739 TGVAVNKPAEFTVDAK_ FLNA_HUMAN  0.693159192 549.6_258.1 TLNAYDHR_330.5_312.2 PAR3_HUMAN  0.647300964 DISEVVTPR_508.3_787.4 CFAB_HUMAN  0.609165621 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 0.60043345 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN  0.596079858 ALQDQLVLVAAK_634.9_ ANGT_HUMAN  0.579034994 289.2 ALVLELAK_428.8_672.4 INHBE_HUMAN  0.573458483

TABLE-US-00037 TABLE 36 Random Forest All Early Window Variable Protein MeanDecreaseGini ELIEELVNITQNQK_557.6_ IL13_HUMAN  0.730972421 517.3 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN  0.409808774 AHYDLR_387.7_288.2 FETUA_HUMAN  0.409298983 FSVVYAK_407.2_381.2 FETUA_HUMAN  0.367730833 ITGFLKPGK_320.9_301.2 LBP_HUMAN  0.350485117 VFQFLEK_455.8_811.4 CO5_HUMAN  0.339289475 ELIEELVNITQNQK_557.6_ IL13_HUMAN  0.334303166 618.3 LPNNVLQEK_527.8_844.5 AFAM_HUMAN  0.329800706 IEGNLIFDPNNYLPK_ APOB_HUMAN  0.325596677 874.0_414.2 ITGFLKPGK_320.9_429.3 LBP_HUMAN 0.31473104 FLNWIK_410.7_560.3 HABP2_HUMAN  0.299810081 LIQDAVTGLTVNGQITGDK_ ITIH3_HUMAN  0.295613448 972.0_798.4 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN  0.292212699 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN  0.285812225 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN  0.280857718 FSVVYAK_407.2_579.4 FETUA_HUMAN  0.278531322 DADPDTFFAK_563.8_825.4 AFAM_HUMAN  0.258938798 AHYDLR_387.7_566.3 FETUA_HUMAN  0.256160046 QALEEFQK_496.8_680.3 CO8B_HUMAN  0.245543641 HTLNQIDEVK_598.8_951.5 FETUA_HUMAN  0.239528081 TASDFITK_441.7_781.4 GELS_HUMAN  0.227485958 VFQFLEK_455.8_276.2 CO5_HUMAN  0.226172392 DVLLLVHNLPQNLPGYFWYK_ PSG9_HUMAN  0.218613384 810.4_967.5 VQTAHFK_277.5_431.2 CO8A_HUMAN  0.217171548 SFRPFVPR_335.9_635.3 LBP_HUMAN  0.214798112 HFQNLGK_422.2_527.2 AFAM_HUMAN  0.211756476 SVSLPSLDPASAK_636.4_ APOB_HUMAN  0.211319422 473.3 FGFGGSTDSGPIR_649.3_ ADA12_HUMAN  0.206574494 745.4 HFQNLGK_422.2_285.1 AFAM_HUMAN  0.204024196 AVLHIGEK_289.5_348.7 THBG_HUMAN  0.201102917

TABLE-US-00038 TABLE 37 Random Forest SummedGini Early Window Transition Protein SumBestGini ELIEELVNITQNQK_557.6_517.3 IL13_HUMAN 242.5373659 VFQFLEK_455.8_811.4 CO5_HUMAN 115.1113943 FLNWIK_410.7_560.3 HABP2_HUMAN 107.4572447 ITLPDFTGDLR_624.3_288.2 LBP_HUMAN 104.0742727 LIQDAVTGLTVNGQITGDK_972.0_798.4 ITIH3_HUMAN 103.3238077 DAGLSWGSAR_510.3_390.2 NEUR4_HUMAN 70.4151533 AHYDLR_387.7_288.2 FETUA_HUMAN 140.2670822 FSVVYAK_407.2_381.2 FETUA_HUMAN 121.3664352 LPNNVLQEK_527.8_844.5 AFAM_HUMAN 115.5211679 ITGFLKPGK_320.9_429.3 LBP_HUMAN 114.9512704 ITGFLKPGK_320.9_301.2 LBP_HUMAN 112.916627 IEGNLIFDPNNYLPK_874.0_414.2 APOB_HUMAN 52.21169288 VQTAHFK_277.5_431.2 CO8A_HUMAN 144.5237215 TLLPVSKPEIR_418.3_288.2 CO5_HUMAN 96.16982897 QALEEFQK_496.8_680.3 CO8B_HUMAN 85.35050759 FSVVYAK_407.2_579.4 FETUA_HUMAN 73.23969945 ELIEELVNITQNQK_557.6_618.3 IL13_HUMAN 61.61450671 TASDFITK_441.7_781.4 GELS_HUMAN 61.32155633 DVLLLVHNLPQNLPGYFWYK_810.4_967.5 PSG9_HUMAN 99.68404123 AVLHIGEK_289.5_348.7 THBG_HUMAN 69.96748485 ITLPDFTGDLR_624.3_920.5 LBP_HUMAN 56.66810872 WWGGQPLWITATK_772.4_929.5 ENPP2_HUMAN 56.54173176 VQEAHLTEDQIFYFPK_655.7_701.4 CO8G_HUMAN 47.92505575 DADPDTFFAK_563.8_825.4 AFAM_HUMAN 40.34147696 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN 145.0311483 FGFGGSTDSGPIR_649.3_745.4 ADA12_HUMAN 109.4072996 FLPCENK_454.2_550.2 IL10_HUMAN 105.7756691 VQTAHFK_277.5_502.3 CO8A_HUMAN 101.5877845 VFQFLEK_455.8_276.2 CO5_HUMAN 95.71159157 TYLHTYESEI_628.3_908.4 ENPP2_HUMAN 94.92157517 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN 90.67568777 NKPGVYTDVAYYLAWIR_677.0_545.3 FA12_HUMAN 90.35890105 LEEHYELR_363.5_580.3 PAI2_HUMAN 88.44833508 HPWIVHWDQLPQYQLNR_744.0_1047.0 KS6A3_HUMAN 88.37680942 HTLNQIDEVK_598.8_951.5 FETUA_HUMAN 87.63064143 LPNNVLQEK_527.8_730.4 AFAM_HUMAN 86.64484642 ALDLSLK_380.2_575.3 ITIH3_HUMAN 83.51201287 YGIEEHGK_311.5_599.3 CXA1_HUMAN 82.47620831 LSSPAVITDK_515.8_830.5 PLMN_HUMAN 81.5433587 LEEHYELR_363.5_288.2 PAI2_HUMAN 79.01571985 NVIQISNDLENLR_509.9_402.3 LEP_HUMAN 78.86670236 SGFSFGFK_438.7_732.4 CO8B_HUMAN 78.71961929 SDLEVAHYK_531.3_617.3 CO8B_HUMAN 78.24005567 NADYSYSVWK_616.8_333.2 CO5_HUMAN 76.07974354 AHYDLR_387.7_566.3 FETUA_HUMAN 74.68253347 GAVHVVVAETDYQSFAVLYLER_822.8_580.3 CO8G_HUMAN 73.75860248 LIENGYFHPVK_439.6_627.4 F13B_HUMAN 73.74965194 ALDLSLK_380.2_185.1 ITIH3_HUMAN 72.760739 WWGGQPLWITATK_772.4_373.2 ENPP2_HUMAN 72.51936706 FGFGGSTDSGPIR_649.3_946.5 ADA12_HUMAN 72.49183198 GLQYAAQEGLLALQSELLR_1037.1_929.5 LBP_HUMAN 67.17588648 HFQNLGK_422.2_527.2 AFAM_HUMAN 66.11702719 YSHYNER_323.5_581.3 HABP2_HUMAN 65.56238612 ISQGEADINIAFYQR_575.6_684.4 MMP8_HUMAN 65.50301246 TGVAVNKPAEFTVDAK_549.6_258.1 FLNA_HUMAN 64.85259525 NIQSVNVK_451.3_674.4 GROA_HUMAN 64.53010225 DALSSVQESQVAQQAR_573.0_672.4 APOC3_HUMAN 64.12149927 SLLQPNK_400.2_599.4 CO8A_HUMAN 62.68167847 SFRPFVPR_335.9_635.3 LBP_HUMAN 61.90157662 NNQLVAGYLQGPNVNLEEK_700.7_999.5 IL1RA_HUMAN 61.54435815 LYYGDDEK_501.7_563.2 CO8A_HUMAN 60.16700473 SWNEPLYHLVTEVR_581.6_716.4 PRL_HUMAN 59.78209065 SGVDLADSNQK_567.3_662.3 VGFR3_HUMAN 58.93982896 GTYLYNDCPGPGQDTDCR_697.0_335.2 TNR1A_HUMAN 58.72963941 HATLSLSIPR_365.6_472.3 VGFR3_HUMAN 57.98669834 FIVGFTR_420.2_261.2 CCL20_HUMAN 57.23165578 QNYHQDSEAAINR_515.9_544.3 FRIH_HUMAN 57.21116697 DVLLLVHNLPQNLPGYFWYK_810.4_594.3 PSG9_HUMAN 56.84150484 FLNWIK_410.7_561.3 HABP2_HUMAN 56.37258274 SLQAFVAVAAR_566.8_487.3 IL23A_HUMAN 56.09012981 HFQNLGK_422.2_285.1 AFAM_HUMAN 56.04480022 GPGEDFR_389.2_322.2 PTGDS_HUMAN 55.7583763 NKPGVYTDVAYYLAWIR_677.0_821.5 FA12_HUMAN 55.53857645 LIQDAVTGLTVNGQITGDK_972.0_640.4 ITIH3_HUMAN 55.52577583 YYGYTGAFR_549.3_450.3 TRFL_HUMAN 54.27147366 TLNAYDHR_330.5_312.2 PAR3_HUMAN 54.19190934 IQTHSTTYR_369.5_627.3 F13B_HUMAN 54.18950583 TASDFITK_441.7_710.4 GELS_HUMAN 54.1056456 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN 53.8997252 DADPDTFFAK_563.8_302.1 AFAM_HUMAN 53.85914848 SVSLPSLDPASAK_636.4_473.3 APOB_HUMAN 53.41996191 TTSDGGYSFK_531.7_860.4 INHA_HUMAN 52.24655536 AFTECCVVASQLR_770.9_574.3 CO5_HUMAN 51.67853429 ELPQSIVYK_538.8_409.2 FBLN3_HUMAN 51.35853002 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN 51.23842124 FQLSETNR_497.8_605.3 PSG2_HUMAN 51.01576848 GSLVQASEANLQAAQDFVR_668.7_806.4 ITIH1_HUMAN 50.81923338 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN 50.54425114 ECEELEEK_533.2_405.2 IL15_HUMAN 50.41977421 NADYSYSVWK_616.8_769.4 CO5_HUMAN 50.36434595 SLLQPNK_400.2_358.2 CO8A_HUMAN 49.75593162 LIEIANHVDK_384.6_683.4 ADA12_HUMAN 49.43389721 DISEVVTPR_508.3_787.4 CFAB_HUMAN 49.00234897 AEVIWTSSDHQVLSGK_586.3_300.2 PD1L1_HUMAN 48.79028835 SGVDLADSNQK_567.3_591.3 VGFR3_HUMAN 48.70665587 SILFLGK_389.2_201.1 THBG_HUMAN 48.5997957 AVLHIGEK_289.5_292.2 THBG_HUMAN 48.4605866 QLYGDTGVLGR_589.8_501.3 CO8G_HUMAN 48.11414904 FSLVSGWGQLLDR_493.3_516.3 FA7_HUMAN 47.59635333 DSPVLIDFFEDTER_841.9_399.2 HRG_HUMAN 46.83840473 INPASLDK_429.2_630.4 C163A_HUMAN 46.78947931 GAVHVVVAETDYQSFAVLYLER_822.8_863.5 CO8G_HUMAN 46.66185339 FLQEQGHR_338.8_497.3 CO8G_HUMAN 46.64415952 LNIGYIEDLK_589.3_837.4 PAI2_HUMAN 46.5879123 LSSPAVITDK_515.8_743.4 PLMN_HUMAN 46.2857838 GLQYAAQEGLLALQSELLR_1037.1_858.5 LBP_HUMAN 45.7427767 SDGAKPGPR_442.7_213.6 COLI_HUMAN 45.27828366 GYQELLEK_490.3_502.3 FETA_HUMAN 43.52928868 GGEGTGYFVDFSVR_745.9_869.5 HRG_HUMAN 43.24514327 ADLFYDVEALDLESPK_913.0_447.2 HRG_HUMAN 42.56268679 ADLFYDVEALDLESPK_913.0_331.2 HRG_HUMAN 42.48967422 EAQLPVIENK_570.8_699.4 PLMN_HUMAN 42.21213429 SILFLGK_389.2_577.4 THBG_HUMAN 42.03379581 HTLNQIDEVK_598.8_958.5 FETUA_HUMAN 41.98377176 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN 41.89547273 FLPCENK_454.2_390.2 IL10_HUMAN 41.66612478 LIEIANHVDK_384.6_498.3 ADA12_HUMAN 41.50878046 DEIPHNDIALLK_459.9_510.8 HABP2_HUMAN 41.27830935 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN 41.00430596 YISPDQLADLYK_713.4_277.2 ENOA_HUMAN 40.90053801 SLPVSDSVLSGFEQR_810.9_836.4 CO8G_HUMAN 40.62020941 DGSPDVTTADIGANTPDATK_973.5_531.3 PGRP2_HUMAN 40.33913091 NTGVISVVTTGLDR_716.4_662.4 CADH1_HUMAN 40.05291612 ALVLELAK_428.8_672.4 INHBE_HUMAN 40.01646465 YEFLNGR_449.7_293.1 PLMN_HUMAN 39.83344278 WGAAPYR_410.7_577.3 PGRP2_HUMAN 39.52766213 TFLTVYWTPER_706.9_401.2 ICAM1_HUMAN 39.13662034 SEYGAALAWEK_612.8_845.5 CO6_HUMAN 38.77511119 VGVISFAQK_474.8_693.4 TFR2_HUMAN 38.5823457 IIEVEEEQEDPYLNDR_996.0_777.4 FBLN1_HUMAN 38.30913304 TGYYFDGISR_589.8_694.4 FBLN1_HUMAN 38.30617106 LQGTLPVEAR_542.3_571.3 CO5_HUMAN 37.93064544 DSPVLIDFFEDTER_841.9_512.3 HRG_HUMAN 37.4447737 AALAAFNAQNNGSNFQLEEISR_789.1_746.4 FETUA_HUMAN 37.02483715 DGSPDVTTADIGANTPDATK_973.5_844.4 PGRP2_HUMAN 36.59864788 ILILPSVTR_506.3_785.5 PSGx_HUMAN 36.43814815 SVSLPSLDPASAK_636.4_885.5 APOB_HUMAN 36.27689491 TLAFVR_353.7_492.3 FA7_HUMAN 36.18771771 VAPGVANPGTPLA_582.3_555.3 A6NIT4_HUMAN 35.70677357 HELTDEELQSLFTNFANVVDK_817.1_906.5 AFAM_HUMAN 35.14441609 AGLLRPDYALLGHR_518.0_369.2 PGRP2_HUMAN 35.13047098 GDTYPAELYITGSILR_885.0_1332.8 F13B_HUMAN 34.97832404 LFIPQITR_494.3_727.4 PSG9_HUMAN 34.76811249 GYQELLEK_490.3_631.4 FETA_HUMAN 34.76117605 VSEADSSNADWVTK_754.9_533.3 CFAB_HUMAN 34.49787512 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN 34.48448691 SFRPFVPR_335.9_272.2 LBP_HUMAN 34.27529415 ILDGGNK_358.7_490.2 CXCL5_HUMAN 34.2331388 EANQSTLENFLER_775.9_678.4 IL4_HUMAN 34.14295797 DFNQFSSGEK_386.8_189.1 FETA_HUMAN 34.05459951 IEEIAAK_387.2_660.4 CO5_HUMAN 33.93778148 TEFLSNYLTNVDDITLVPGTLGR_846.8_600.3 ENPP2_HUMAN 33.87864446 LPATEKPVLLSK_432.6_347.2 HYOU1_HUMAN 33.69005522 FLQEQGHR_338.8_369.2 CO8G_HUMAN 33.61179024 APLTKPLK_289.9_357.2 CRP_HUMAN 33.59900279 YSHYNER_323.5_418.2 HABP2_HUMAN 33.50888447 TSYQVYSK_488.2_787.4 C163A_HUMAN 33.11650018 IALGGLLFPASNLR_481.3_657.4 SHBG_HUMAN 33.02974341 TGISPLALIK_506.8_741.5 APOB_HUMAN 32.64471573 LYYGDDEK_501.7_726.3 CO8A_HUMAN 32.60782458 IVLSLDVPIGLLQILLEQAR_735.1_503.3 UCN2_HUMAN 32.37907686 EAQLPVIENK_570.8_329.2 PLMN_HUMAN 32.34049256 TGYYFDGISR_589.8_857.4 FBLN1_HUMAN 32.14526507 VGVISFAQK_474.8_580.3 TFR2_HUMAN 32.11753213 FQSVFTVTR_542.8_623.4 C1QC_HUMAN 32.11360444 TSDQIHFFFAK_447.6_659.4 ANT3_HUMAN 31.95867038 IAPQLSTEELVSLGEK_857.5_333.2 AFAM_HUMAN 31.81531364 EVFSKPISWEELLQ_852.9_260.2 FA40A_HUMAN 31.36698726 DEIPHNDIALLK_459.9_260.2 HABP2_HUMAN 31.1839869 NYFTSVAHPNLFIATK_608.3_319.2 ILIA_HUMAN 31.09867061 ITENDIQIALDDAK_779.9_632.3 APOB_HUMAN 30.77026845 DTYVSSFPR_357.8_272.2 TCEA1_HUMAN 30.67784731 TDAPDLPEENQAR_728.3_843.4 CO5_HUMAN 30.66251941 LFYADHPFIFLVR_546.6_647.4 SERPH_HUMAN 30.65831566 TEQAAVAR_423.2_487.3 FA12_HUMAN 30.44356842 AVGYLITGYQR_620.8_737.4 PZP_HUMAN 30.36425528 HSHESQDLR_370.2_288.2 HRG_HUMAN 30.34684703 IALGGLLFPASNLR_481.3_412.3 SHBG_HUMAN 30.34101643 IAQYYYTFK_598.8_884.4 F13B_HUMAN 30.23453833 SLPVSDSVLSGFEQR_810.9_723.3 CO8G_HUMAN 30.11396489 IIGGSDADIK_494.8_762.4 C1S_HUMAN 30.06572687 QTLSWTVTPK_580.8_545.3 PZP_HUMAN 30.04139865 HYFIAAVER_553.3_658.4 FA8_HUMAN 29.80239884 QVCADPSEEWVQK_788.4_374.2 CCL3_HUMAN 29.61435573 DLHLSDVFLK_396.2_366.2 CO6_HUMAN 29.60077507 NIQSVNVK_451.3_546.3 GROA_HUMAN 29.47619619 QTLSWTVTPK_580.8_818.4 PZP_HUMAN 29.40047934 HSHESQDLR_370.2_403.2 HRG_HUMAN 29.32242262 LLEVPEGR_456.8_356.2 C1S_HUMAN 29.14169137 LIENGYFHPVK_439.6_343.2 F13B_HUMAN 28.63056809 EDTPNSVWEPAK_686.8_630.3 C1S_HUMAN 28.61352686 AFTECCVVASQLR_770.9_673.4 CO5_HUMAN 28.57830281 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN 28.27203693 VSFSSPLVAISGVALR_802.0_715.4 PAPP1_HUMAN 28.13008712 DPDQTDGLGLSYLSSHIANVER_796.4_456.2 GELS_HUMAN 28.06549895 VVGGLVALR_442.3_784.5 FA12_HUMAN 28.00684006 NEIVFPAGILQAPFYTR_968.5_357.2 ECE1_HUMAN 27.97758456 QVCADPSEEWVQK_788.4_275.2 CCL3_HUMAN 27.94276837 LQDAGVYR_461.2_680.3 PD1L1_HUMAN 27.88063261 IQTHSTTYR_369.5_540.3 F13B_HUMAN 27.68873826 TPSAAYLWVGTGASEAEK_919.5_849.4 GELS_HUMAN 27.66889639 ALALPPLGLAPLLNLWAKPQGR_770.5_256.2 SHBG_HUMAN 27.63105727 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN 27.63097319 IEEIAAK_387.2_531.3 CO5_HUMAN 27.52427934 TAVTANLDIR_537.3_288.2 CHL1_HUMAN 27.44246841 VSEADSSNADWVTK_754.9_347.2 CFAB_HUMAN 27.43976782 ITENDIQIALDDAK_779.9_873.5 APOB_HUMAN 27.39263522 SSNNPHSPIVEEFQVPYNK_729.4_521.3 C15_HUMAN 27.34493617 HPWIVHWDQLPQYQLNR_744.0_918.5 K56A3_HUMAN 27.19681613 TPSAAYLWVGTGASEAEK_919.5_428.2 GELS_HUMAN 27.17319953 AFLEVNEEGSEAAASTAVVIAGR_764.4_614.4 ANT3_HUMAN 27.10487351 WGAAPYR_410.7_634.3 PGRP2_HUMAN 27.09930054 IEVNESGTVASSSTAVIVSAR_693.0_545.3 PAI1_HUMAN 27.02567296 AEAQAQYSAAVAK_654.3_908.5 ITIH4_HUMAN 26.98305259 VPLALFALNR_557.3_917.6 PEPD_HUMAN 26.96988826 TLEAQLTPR_514.8_685.4 HEP2_HUMAN 26.94672621 QALEEFQK_496.8_551.3 CO8B_HUMAN 26.67037155 WNFAYWAAHQPWSR_607.3_545.3 PRG2_HUMAN 26.62600679 IYLQPGR_423.7_570.3 ITIH2_HUMAN 26.58752589 FFQYDTWK_567.8_840.4 IGF2_HUMAN 26.39942037 NEIWYR_440.7_357.2 FA12_HUMAN 26.35177282 GGEGTGYFVDFSVR_745.9_722.4 HRG_HUMAN 26.31688167 VGEYSLYIGR_578.8_708.4 SAMP_HUMAN 26.17367498 TAHISGLPPSTDFIVYLSGLAPSIR_871.5_800.5 TENA_HUMAN 26.13688183 GVTGYFTFNLYLK_508.3_260.2 PSG5_HUMAN 26.06007032 DYWSTVK_449.7_620.3 APOC3_HUMAN 26.03765187 YENYTSSFFIR_713.8_756.4 IL12B_HUMAN 25.9096605 YGLVTYATYPK_638.3_334.2 CFAB_HUMAN 25.84440452 LFIPQITR_494.3_614.4 PSG9_HUMAN 25.78081129 YEFLNGR_449.7_606.3 PLMN_HUMAN 25.17159874 SEPRPGVLLR_375.2_454.3 FA7_HUMAN 25.16444381 NSDQEIDFK_548.3_294.2 S10A5_HUMAN 25.12266401 YEVQGEVFTKPQLWP_911.0_293.1 CRP_HUMAN 24.77595195 GVTGYFTFNLYLK_508.3_683.9 PSG5_HUMAN 24.75289081 ISLLLIESWLEPVR_834.5_371.2 CSH_HUMAN 24.72379326 ALLLGWVPTR_563.3_373.2 PAR4_HUMAN 24.68096599 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN 24.53420489 SGAQATWTELPWPHEK_613.3_793.4 HEMO_HUMAN 24.25610995 AQPVQVAEGSEPDGFWEALGGK_758.0_623.4 GELS_HUMAN 24.18769142 DLPHITVDR_533.3_490.3 MMP7_HUMAN 24.02606052 SEYGAALAWEK_612.8_788.4 CO6_HUMAN 24.00163743 AVGYLITGYQR_620.8_523.3 PZP_HUMAN 23.93958524 GFQALGDAADIR_617.3_717.4 TIMP1_HUMAN 23.69249513 YEVQGEVFTKPQLWP_911.0_392.2 CRP_HUMAN 23.67764212 SDGAKPGPR_442.7_459.2 COLI_HUMAN 23.63551614 GFQALGDAADIR_617.3_288.2 TIMP1_HUMAN 23.55832742 IAPQLSTEELVSLGEK_857.5_533.3 AFAM_HUMAN 23.38139357 DTDTGALLFIGK_625.8_217.1 PEDF_HUMAN 23.33375418 LHEAFSPVSYQHDLALLR_699.4_380.2 FA12_HUMAN 23.27455931 IYLQPGR_423.7_329.2 ITIH2_HUMAN 23.19122626

TABLE-US-00039 TABLE 38 Random Forest 32 Middle Window Variable UniProt_ID MeanDecreaseGini SEYGAALAWEK_612.8_788.4 CO6_HUMAN 2.27812193 LLAPSDSPEWLSFDVTGVVR_730.1_430.3 TGFB1_HUMAN  2.080133179 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN  1.952233942 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN  1.518833357 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN  1.482593086 VFQFLEK_455.8_811.4 CO5_HUMAN  1.448810425 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN  1.389922815 YGIEEHGK_311.5_599.3 CXA1_HUMAN  1.386794676 TLAFVR_353.7_492.3 FA7_HUMAN  1.371530925 VLEPTLK_400.3_587.3 VTDB_HUMAN  1.368583173 VLEPTLK_400.3_458.3 VTDB_HUMAN  1.336029064 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN  1.307024357 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN  1.282930911 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN 1.25362163 SEPRPGVLLR_375.2_654.4 FA7_HUMAN  1.205539225 VEHSDLSFSK_383.5_468.2 B2MG_HUMAN  1.201047302 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN  1.189617326 SEYGAALAWEK_612.8_845.5 CO6_HUMAN  1.120706696 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN  1.107036657 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN  1.083264902 IEEIAAK_387.2_660.4 CO5_HUMAN  1.043635292 ALNHLPLEYNSALYSR_621.0_538.3 CO6_HUMAN  0.962643698 TLLPVSKPEIR_418.3_514.3 CO5_HUMAN  0.933440467 TEQAAVAR_423.2_615.4 FA12_HUMAN  0.878933553 DLHLSDVFLK_396.2_260.2 CO6_HUMAN  0.816855601 ALQDQLVLVAAK_634.9_289.2 ANGT_HUMAN  0.812620232 SLQAFVAVAAR_566.8_804.5 IL23A_HUMAN  0.792274782 QGHNSVFLIK_381.6_260.2 HEMO_HUMAN  0.770830031 ALQDQLVLVAAK_634.9_956.6 ANGT_HUMAN  0.767468246 SLDFTELDVAAEK_719.4_874.5 ANGT_HUMAN  0.745827911

TABLE-US-00040 TABLE 39 Random Forest 100 Middle Window Variable UniProt_ID MeanDecreaseGini SEYGAALAWEK_612.8_788.4 CO6_HUMAN   1.241568411 ALNHLPLEYNSALYSR_621.0_696.4 CO6_HUMAN   0.903126414 LLAPSDSPEWLSFDVTGVVR_730._1430.3 TGFB1_HUMAN   0.846216563 ANLINNIFELAGLGK_793.9_299.2 LCAP_HUMAN   0.748261193 VFQFLEK_455.8_811.4 CO5_HUMAN   0.717545171 VEHSDLSFSK_383.5_234.1 B2MG_HUMAN   0.683219617 ELPQSIVYK_538.8_417.7 FBLN3_HUMAN   0.671091545 LNIGYIEDLK_589.3_950.5 PAI2_HUMAN   0.652293621 VLEPTLK_400.3_587.3 VTDB_HUMAN   0.627095631 VNHVTLSQPK_374.9_244.2 B2MG_HUMAN   0.625773888 VLEPTLK_400.3_458.3 VTDB_HUMAN   0.613655529 AQPVQVAEGSEPDGFWEALGGK_758.0_574.3 GELS_HUMAN   0.576305627 TLFIFGVTK_513.3_811.5 PSG4_HUMAN   0.574056825 YGIEEHGK_311.5_599.3 CXA1_HUMAN   0.570270447 VPLALFALNR_557.3_620.4 PEPD_HUMAN   0.556087614 EVFSKPISWEELLQ_852.9_376.2 FA40A_HUMAN   0.531461012 VEHSDLSFSK_383.5_468.2 B2MG_HUMAN   0.531214597 TLAFVR_353.7_492.3 FA7_HUMAN  0.53070743 DALSSVQESQVAQQAR_573.0_502.3 APOC3_HUMAN   0.521633041 SEYGAALAWEK__612.8_845.5 CO6_HUMAN   0.514509661 SLDFTELDVAAEK_719.4_316.2 ANGT_HUMAN  0.50489698 SEPRPGVLLR_375.2_654.4 FA7_HUMAN 0.4824926 LHEAFSPVSYQHDLALLR_699.4_251.2 FA12_HUMAN  0.48217238 TYLHTYESEI_628.3_515.3 ENPP2_HUMAN   0.472286273 AVDIPGLEAATPYR_736.9_399.2 TENA_HUMAN   0.470892051 FSLVSGWGQLLDR_493.3_403.2 FA7_HUMAN   0.465839813 GEVTYTTSQVSK_650.3_750.4 EGLN_HUMAN   0.458736205 VNHVTLSQPK_374.9_459.3 B2MG_HUMAN   0.454348892 HFQNLGK_422.2_527.2 AFAM_HUMAN  0.45127405 YGIEEHGK_311.5_341.2 CXA1_HUMAN   0.430641646

TABLE-US-00041 TABLE 40 Random Forest Protein Middle Window Variable UniProt_ID MeanDecreaseGini SEYGAALAWEK_ CO6_HUMAN 2.09649626 612.8_788.4 LLAPSDSPEWLSF TGFB1_HUMAN 1.27664656 DVTGVVR_ 730.1_430.3 VFQFLEK_ CO5_HUMAN 1.243884833 455.8_811.4 ANLINNIFELAG LCAP_HUMAN 1.231814882 LGK_ 793.9_299.2 VEHSDLSFSK_ B2MG_HUMAN 1.188808078 383.5_234.1 ELPQSIVYK_ FBLN3_HUMAN 1.185075445 538.8_417.7 LNIGYIEDLK_ PAI2_HUMAN 1.122351536 589.3_950.5 VLEPTLK_ VTDB_HUMAN 1.062664798 400.3_458.3 VPLALFALNR_ PEPD_HUMAN 1.019466776 557.3_620.4 TLAFVR_ FA7_HUMAN 0.98797064 353.7_492.3 TLFIFGVTK_ PSG4_HUMAN 0.980159531 513.3_811.5 AQPVQVAEGSEP GELS_HUMAN 0.960286027 DGFWEALGGK 758.0_574.3 DALSSVQESQVA APOC3_HUMAN 0.947091926 QQAR_ 573.0_502.3 YGIEEHGK_ CXA1_HUMAN 0.946937719 311.5_599.3 EVFSKPISWEELLQ_ FA40A_HUMAN 0.916262164 852.9_376.2 LHEAFSPVSYQ FA12_HUMAN 0.891310053 HDLALLR_ 699.4_251.2 SLDFTELDVAAEK_ ANGT_HUMAN 0.884498494 719.4_316.2 TYLHTYESEI_ ENPP2_HUMAN 0.869043942 628.3_515.3 HFQNLGK_ AFAM_HUMAN 0.865435217 422.2_527.2 AVDIPGLEAATPYR_ TENA_HUMAN 0.844842109 736.9_399.2 TLNAYDHR_ PAR3_HUMAN 0.792615068 330.5_312.2 DVLLLVHNLPQNL PSG7_HUMAN 0.763629346 TGHIWYK_ 791.8_310.2 GPITSAAELNDPQ EGLN_HUMAN 0.762305265 SILLR_ 632.4_826.5 VVLSSGSGPGLDL SHBG_HUMAN 0.706312721 PLVLGLPLQLK_ 791.5_598.4 SLQNASAIESILK_ IL3_HUMAN 0.645503581 687.4_860.5 HYINLITR_ NPY_HUMAN 0.62631682 515.3_301.1 VELAPLPSWQPVGK_ ICAM1_HUMAN 0.608991877 760.9_342.2 LQVNTPLVGASLLR_ BPIA1_HUMAN 0.607801279 741.0_925.6 TLEAQLTPR_ HEP2_HUMAN 0.597771074 514.8_814.4 SDGAKPGPR_ COLI_HUMAN 0.582773073 442.7_459.2

TABLE-US-00042 TABLE 41  Random Forest All Middle Window Variable UniProt ID MeanDecreaseGini SEYGAALAWEK_ CO6_HUMAN 0.493373282 612.8_788.4 ALNHLPLEYNSAL CO6_HUMAN 0.382180772 YSR_ 621.0_696.4 VFQFLEK_ CO5_HUMAN 0.260292083 455.8_811.4 LLAPSDSPEWLSFD TGFB1_HUMAN 0.243156718 VTGWR_ 730.1_430.3 NADYSYSVWK_ CO5_HUMAN 0.242388196 616.8_769.4 VLEPTLK_ VTDB_HUMAN 0.238171849 400.3_458.3 VEHSDLSFSK_ B2MG_HUMAN 0.236873731 383.5_234.1 ELPQSIVYK_ FBLN3_HUMAN 0.224727161 538.8_417.7 VLEPTLK_ VTDB_HUMAN 0.222105614 400.3_587.3 TLFIFGVTK_ PSG4_HUMAN 0.210807574 513.3_811.5 ANLINNIFELAGLGK_ LCAP_HUMAN 0.208714978 793.9_299.2 LNIGYIEDLK_ PAI2_HUMAN 0.208027555 589.3_950.5 SEYGAALAWEK_ CO6_HUMAN 0.197362212 612.8_845.5 VNHVTLSQPK_ B2MG_HUMAN 0.195728091 374.9_244.2 YGIEEHGK_ CXA1_HUMAN 0.189969499 311.5_599.3 HFQNLGK_ AFAM_HUMAN 0.189572857 422.2_527.2 AGITIPR_ IL17_HUMAN 0.188351054 364.2_486.3 AQPVQVAEGSE GELS_HUMAN 0.185069517 PDGFWEALGGK_ 758.0_574.3 SLDFTELDVAAEK_ ANGT_HUMAN 0.173688295 719.4_316.2 TLAFVR_ FA7_HUMAN 0.170636045 353.7_492.3 SEPRPGVLLR_ FA7_HUMAN 0.170608352 375.2_654.4 TLLIANETLR_ IL5_HUMAN 0.16745571 572.3_703.4 ALNHLPLEYNS CO6_HUMAN 0.161514946 ALYSR_ 621.0_538.3 LHEAFSPVSYQ FA12_HUMAN 0.15852146 HDLALLR_ 699.4_251.2 DGSPDVTTADI PGRP2_HUMAN 0.154028378 GANTPDATK_ 973.5_844.4 VPLALFALNR_ PEPD_HUMAN 0.153725879 557.3_620.4 AVDIPGLEAATPYR_ TENA_HUMAN 0.150920884 736.9_399.2 YGIEEHGK_ CXA1_HUMAN 0.150319671 311.5_341.2 FSLVSGWGQLLDR_ FA7_HUMAN 0.144781622 493.3_403.2 IEEIAAK_ CO5_HUMAN 0.141983196 387.2_660.4

TABLE-US-00043 TABLE 42 Random Forest 32 Middle-Late Window Variable UniProt_ID MeanDecreaseGini VPLALFALNR_ PEPD_HUMAN 4.566619475 557.3_620.4 VFQFLEK_ CO5_HUMAN 3.062474666 455.8_811.4 AQPVQVAEGSEP GELS_HUMAN 3.033740627 DGFWEALGGK_ 758.0_574.3 LIEIANHVDK_ ADA12_HUMAN 2.825082394 384.6_498.3 DALSSVQESQVA APOC3_HUMAN 2.787777983 QQAR_ 573.0_502.3 TLAFVR_ FA7_HUMAN 2.730532075 353.7_492.3 ALNHLPLEYNSA CO6_HUMAN 2.671290375 LYSR_ 621.0_696.4 AVYEAVLR_ PEPD_HUMAN 2.621357053 460.8_587.4 SEPRPGVLLR_ FA7_HUMAN 2.57568964 375.2_654.4 TYLHTYESEI_ ENPP2_HUMAN 2.516708906 628.3_515.3 ALNHLPLEYNS CO6_HUMAN 2.497348374 ALYSR_ 621.0_538.3 LIEIANHVDK_ ADA12_HUMAN 2.457401462 384.6_683.4 YGIEEHGK_ CXA1_HUMAN 2.396824268 311.5_599.3 VLEPTLK_ VTDB_HUMAN 2.388105564 400.3_587.3 SEYGAALAWEK_ CO6_HUMAN 2.340473883 612.8_788.4 WSAGLTSSQVD CBG_HUMAN 2.332007976 LYIPK_ 883.0_515.3 FGFGGSTDSGPIR_ ADA12_HUMAN 2.325669514 649.3_946.5 SEYGAALAWEK_ CO6_HUMAN 2.31761671 612.8_845.5 QINSYVK_ CBG_HUMAN 2.245221163 426.2_496.3 QINSYVK_ CBG_HUMAN 2.212307699 426.2_610.3 TEQAAVAR_ FA12_HUMAN 2.105860336 423.2_615.4 AVYEAVLR_ PEPD_HUMAN 2.098321893 460.8_750.4 TEQAAVAR_ FA12_HUMAN 2.062684763 423.2_487.3 DFNQFSSGEK_ FETA_HUMAN 2.05160689 386.8_333.2 SLQAFVAVAAR_ IL23A_HUMAN 1.989521006 566.8_804.5 SLDFTELDVAAEK_ ANGT_HUMAN 1.820628782 719.4_316.2 DPTFIPAPIQAK_ ANGT_HUMAN 1.763514326 433.2_556.3 DPTFIPAPIQAK_ ANGT_HUMAN 1.760870392 433.2_461.2 VLEPTLK_ VTDB_HUMAN 1.723389354 400.3_458.3 YENYTSSFFIR_ IL12B_HUMAN 1.63355187 713.8_756.4

TABLE-US-00044 TABLE 43 Random Forest 100 Middle-Late Window Variable UniProt_ID MeanDecreaseGini VPLALFALNR_ PEPD_HUMAN 1.995805024 557.3_620.4 VFQFLEK_ CO5_HUMAN 1.235926416 455.8_811.4 DALSSVQESQVAQQAR_ APOC3_HUMAN 1.187464899 573.0_502.3 EVFSKPISWEELLQ_ FA40A_HUMAN 1.166642578 852.9_376.2 AQPVQVAEGSEPDGFW GELS_HUMAN 1.146077071 EALGGK_ 758.0_574.3 TLAFVR_ FA7_HUMAN 1.143038275 353.7_492.3 ANLINNIFELAGLGK_ LCAP_HUMAN 1.130656591 793.9_299.2 ALNHLPLEYNSALYSR_ CO6_HUMAN 1.098305298 621.0_538.3 ELPQSIVYK_ FBLN3_HUMAN 1.096715712 538.8_417.7 LLAPSDSPEWLSFDV TGFB1_HUMAN 1.086171713 TGWR_ 730.1_430.3 YGIEEHGK_ CXA1_HUMAN 1.071880823 311.5_341.2 ALNHLPLEYNSALY CO6_HUMAN 1.062278869 SR_ 621.0_696.4 TQILEWAAER_ EGLN_HUMAN 1.059019017 608.8_761.4 AVYEAVLR_ PEPD_HUMAN 1.057920661 460.8_587.4 AEIEYLEK_ LYAM1_HUMAN 1.038388955 497.8_552.3 SEPRPGVLLR_ FA7_HUMAN 1.028275728 375.2_654.4 AVDIPGLEAATPYR_ TENA_HUMAN 1.026032369 736.9_399.2 LIEIANHVDK_ ADA12_HUMAN 1.015065282 384.6_498.3 YGIEEHGK_ CXA1_HUMAN 0.98667651 311.5_599.3 VLEPTLK_ VTDB_HUMAN 0.970330675 400.3_587.3 DVLLLVHNLPQNLT PSG7_HUMAN 0.934747674 GHIWYK_ 791.8_883.0 TAHISGLPPSTDFI TENA_HUMAN 0.889111923 VYLSGLAPSIR_ 871.5_472.3 TLNAYDHR_ PAR3_HUMAN 0.887605636 330.5_312.2 FGFGGSTDSGPIR_ ADA12_HUMAN 0.884305889 649.3_946.5 LIEIANHVDK_ ADA12_HUMAN 0.880889836 384.6_683.4 SEYGAALAWEK_ CO6_HUMAN 0.863585472 612.8_788.4 TYLHTYESEI_ ENPP2_HUMAN 0.849232356 628.3_515.3 FGFGGSTDSGPIR_ ADA12_HUMAN 0.843334824 649.3_745.4 SEYGAALAWEK_ CO6_HUMAN 0.842319271 612.8_845.5 TPSAAYLWVGTGA GELS_HUMAN 0.828959173 SEAEK_ 919.5_849.4

TABLE-US-00045 TABLE 44 Random Forest Protein Middle-Late Window Variable UniProt_ID MeanDecreaseGini VPLALFALNR_ PEPD_HUMAN 3.202123047 557.3_620.4 ANUNNIFELAGLGK_ LCAP_HUMAN 2.100447309 793.9_299.2 VFQFLEK_ CO5_HUMAN 2.096157529 455.8_811.4 AQPVQVAEGSEP GELS_HUMAN 2.052960939 DGFWEALGGK_ 758.0_574.3 ALNHLPLEYNSAL CO6_HUMAN 2.046139797 YSR_ 621.0_696.4 TQILEWAAER_ EGLN_HUMAN 1.99287941 608.8_761.4 ELPQSIVYK_ FBLN3_HUMAN 1.920894959 538.8_417.7 TGVAVNKPAEFTV FLNA_HUMAN 1.917665697 DAK_ 549.6_258.1 SEPRPGVLLR_ FA7_HUMAN 1.883557705 375.2_654.4 DALSSVQESQVAQ APOC3_HUMAN 1.870232155 QAR_ 573.0_502.3 EVFSKPISWEELL FA40A_HUMAN 1.869000136 Q_852.9_376.2 LIEIANHVDK_ ADA12_HUMAN 1.825457092 384.6_683.4 VLEPTLK_ VTDB_HUMAN 1.695327774 400.3_587.3 TEQAAVAR_ FA12_HUMAN 1.685013152 423.2_615.4 LLAPSDSPEWLS TGFB1_HUMAN 1.684068039 FDVTGWR_ 730.1_430.3 TLNAYDHR_ PAR3_HUMAN 1.673758239 330.5_312.2 AVDIPGLEAATP TENA_HUMAN 1.648896853 YR_ 736.9_399.2 DVLLLVHNLPQN PSG7_HUMAN 1.648146088 LTGHIWYK_ 791.8_883.0 AEIEYLEK_ LYAM1_HUMAN 1.645833005 497.8_552.3 TYLHTYESEI_ ENPP2_HUMAN 1.639121965 628.3_515.3 AGLLRPDYALLG PGRP2_HUMAN 1.610227875 HR_ 518.0_595.4 YGIEEHGK_ CXA1_HUMAN 1.606978339 311.5_599.3 QINSYVK_ CBG_HUMAN 1.554905578 426.2_496.3 LTTVDIVTLR_ IL2RB_HUMAN 1.484081016 565.8_815.5 AALAAFNAQNNGS FETUA_HUMAN 1.43173022 NFQLEEISR_ 789.1_746.4 AEVIWTSSDHQVL PD1L1_HUMAN 1.394857397 SGK_ 586.3_300.2 ALEQDLPVNIK_ CNDP1_HUMAN 1.393464547 620.4_570.4 DFNQFSSGEK_ FETA_HUMAN 1.374296237 386.8_333.2 TSYQVYSK_ C163A_HUMAN 1.36141387 488.2_787.4 TLEAQLTPR_ HEP2_HUMAN 1.311118611 514.8_685.4

TABLE-US-00046 TABLE 45 Random Forest All Middle-Late Window Variable UniProt_ID MeanDecreaseGini VPLALFALNR_ PEPD_HUMAN 0.685165163 557.3_620.4 VFQFLEK_ CO5_HUMAN 0.426827804 455.8_811.4 ALNHLPLEYNSA CO6_HUMAN 0.409942379 LYSR_ 621.0_538.3 YGIEEHGK_ CXA1_HUMAN 0.406589512 311.5_341.2 ALNHLPLEYNSA CO6_HUMAN 0.402152062 LYSR_ 621.0_696.4 AQPVQVAEGSEP GELS_HUMAN 0.374861014 DGFWEALGGK_ 758.0_574.3 ANLINNIFELAG LCAP_HUMAN 0.367089422 LGK_ 793.9_299.2 TQILEWAAER_ EGLN_HUMAN 0.353757524 608.8_761.4 AVYEAVLR_ PEPD_HUMAN 0.350518668 460.8_587.4 TLAFVR_ FA7_HUMAN 0.344669505 353.7_492.3 SEPRPGVLLR_ FA7_HUMAN 0.338752336 375.2_654.4 LIEIANHVDK_ ADA12_HUMAN 0.321850027 384.6_683.4 ELPQSIVYK_ FBLN3_HUMAN 0.301819017 538.8_417.7 EVFSKPISWEEL FA40A_HUMAN 0.299561811 LQ_ 852.9_376.2 LIEIANHVDK_ ADA12_HUMAN 0.298253589 384.6_498.3 VLEPTLK_ VTDB_HUMAN 0.296206088 400.3_587.3 YGIEEHGK_ CXA1_HUMAN 0.295621408 311.5_599.3 DVLLLVHNLPQN PSG7_HUMAN 0.292937475 LTGHIWYK_ 791.8_883.0 TYLHTYESEI_ ENPP2_HUMAN 0.275902848 628.3_515.3 DALSSVQESQVA APOC3_HUMAN 0.275664578 QQAR_ 573.0_502.3 FGFGGSTDSG ADA12_HUMAN 0.27120436 PIR_ 649.3_745.4 AVDIPGLEAAT TENA_HUMAN 0.266568271 PYR_ 736.9_399.2 TGVAVNKPAEFT FLNA_HUMAN 0.262537889 VDAK_ 549.6_258.1 TLNAYDHR_ PAR3_HUMAN 0.259901193 330.5_312.2 IYLQPGR_ ITIH2_HUMAN 0.259086112 423.7_329.2 AEVIWTSSDHQV PD1L1_HUMAN 0.25722354 LSGK_ 586.3_300.2 VPSHAVVAR_ TRFL_HUMAN 0.256151812 312.5_515.3 SEYGAALAWEK_ CO6_HUMAN 0.251704855 612.8_845.5 FGFGGSTDSGPIR_ ADA12_HUMAN 0.249400642 649.3_946.5 SEYGAALAWEK_ CO6_HUMAN 0.245930393 612.8_788.4

TABLE-US-00047 TABLE 46 Random Forest 32 Late Window Variable UniProt_D MeanDecreaseGini AVYEAVLR_ PEPD_HUMAN 1.889521223 460.8_587.4 AEIEYLEK_ LYAM1_HUMAN 1.75233545 497.8_552.3 AALAAFNAQNNGS FETUA_HUMAN 1.676813493 NFQLEEISR_ 789.1_746.4 TGVAVNKPAEFTV FLNA_HUMAN 1.600684153 DAK_ 549.6_258.1 AVYEAVLR_ PEPD_HUMAN 1.462889662 460.8_750.4 LIEIANHVDK_ ADA12_HUMAN 1.364115361 384.6_683.4 VPLALFALNR_ PEPD_HUMAN 1.324317148 557.3_620.4 QINSYVK_ CBG_HUMAN 1.305932064 426.2_610.3 ITQDAQLK_ CBG_HUMAN 1.263533228 458.8_702.4 FGFGGSTDSGPIR_ ADA12_HUMAN 1.245153376 649.3_745.4 LIEIANHVDK_ ADA12_HUMAN 1.236529173 384.6_498.3 QINSYVK_ CBG_HUMAN 1.221866266 426.2_496.3 YSHYNER_ HABP2_HUMAN 1.169575572 323.5_418.2 YYGYTGAFR_ TRFL_HUMAN 1.126684146 549.3_450.3 VGVISFAQK_ TFR2_HUMAN 1.075283855 474.8_580.3 VFQYIDLHQDEFV CNDP1_HUMAN 1.07279097 QTLK_ 708.4_375.2 SPEAEDPLGVER_ Z512B_HUMAN 1.05759256 649.8_314.1 DEIPHNDIALLK_ HABP2_HUMAN 1.028933332 459.9_510.8 ALEQDLPVNIK_ CNDP1_HUMAN 1.014443799 620.4_798.5 ALEQDLPVNIK_ CNDP1_HUMAN 1.010573267 620.4_570.4 ILDGGNK_ CXCL5_HUMAN 0.992175141 358.7_603.3 TSYQVYSK_ C163A_HUMAN 0.95649585 488.2_787.4 YENYTSSFFIR_ IL12B_HUMAN 0.955085198 713.8_756.4 SETEIHQGFQHL CBG_HUMAN 0.944726739 HQLFAK_ 717.4_447.2 TLPFSR_ LYAM1_HUMAN 0.944426109 360.7_506.3 VLSSIEQK_ 1433S_HUMAN 0.933902495 452.3_691.4 AEIEYLEK_ LYAM1_HUMAN 0.891235263 497.8_389.2 GTYLYNDCPGPG TNR1A_HUMAN 0.87187037 QDTDCR_ 697.0_666.3 SGVDLADSNQK_ VGFR3_HUMAN 0.869821307 567.3_662.3 SGVDLADSNQK_ VGFR3_HUMAN 0.839946466 567.3_591.3

TABLE-US-00048 TABLE 47 Random Forest 100 Late Window Variable UniProt_ID MeanDecreaseGini AVYEAVLR_ PEPD_HUMAN 0.971695767 460.8_587.4 AEIEYLEK_ LYAM1_HUMAN 0.920098693 497.8_552.3 TGVAVNKPAEFTVDAK_ FLNA_HUMAN 0.786924487 549.6_258.1 AVYEAVLR_ PEPD_HUMAN 0.772867983 460.8_750.4 AALAAFNAQNNGSNFQ FETUA_HUMAN 0.744138513 LEEISR_ 789.1_746.4 AYSDLSR_ SAMP_HUMAN 0.736078079 406.2_375.2 VPLALFALNR_ PEPD_HUMAN 0.681784822 557.3_620.4 QINSYVK_ CBG_HUMAN 0.585819307 426.2_610.3 LIEIANHVDK_ ADA12_HUMAN 0.577161158 384.6_498.3 FGFGGSTDSGPIR_ ADA12_HUMAN 0.573055613 649.3_745.4 WSAGLTSSQVDLY CBG_HUMAN 0.569156128 IPK_ 883.0_515.3 ITQDAQLK_ CBG_HUMAN 0.551017844 458.8_702.4 LIEIANHVDK_ ADA12_HUMAN 0.539330047 384.6_683.4 YYGYTGAFR_ TRFL_HUMAN 0.527652175 549.3_450.3 VFQYIDLHQDEFV CNDP1_HUMAN 0.484155289 QTLK_ 708.4_375.2 FQLPGQK_ PSG1_HUMAN 0.480394031 409.2_429.2 AVDIPGLEAATPYR_ TENA_HUMAN 0.475252565 736.9_286.1 QINSYVK_ CBG_HUMAN 0.4728541 426.2_496.3 YISPDQLADLYK_ ENOAHUMAN 0.470079977 713.4_277.2 TLPFSR_ LYAM1_HUMAN 0.46881451 360.7_506.3 SPEAEDPLGVER_ Z512B_HUMAN 0.4658941 649.8_314.1 ALEQDLPVNIK_ CNDP1_HUMAN 0.463604174 620.4_798.5 YSHYNER_ HABP2_HUMAN 0.453076307 323.5_418.2 VGVISFAQK_ TFR2_HUMAN 0.437768219 474.8_580.3 LQDAGVYR_ PD1L1_HUMAN 0.428524689 461.2_680.3 AEIEYLEK_ LYAM1_HUMAN 0.42041448 497.8_389.2 TSYQVYSK_ C163A_HUMAN 0.419411932 488.2_787.4 SVVLIPLGAVDD CNDP1_HUMAN 0.415325735 GEHSQNEK_ 703.0_798.4 ALEQDLPVNIK_ CNDP1_HUMAN 0.407951733 620.4_570.4 ILDGGNK_ CXCL5_HUMAN 0.401059572 358.7_603.3

TABLE-US-00049 TABLE 48 Random Forest Protein Late Window Variable UniProt_D MeanDecreaseGini AVYEAVLR_ PEPD_HUMAN 1.836010146 460.8_587.4 AEIEYLEK_ LYAM1_HUMAN 1.739802548 497.8_552.3 AALAAFNAQNNG FETUA_HUMAN 1.455337749 SNFQ LEEISR_ 789.1_746.4 TGVAVNKPAEFT FLNA_HUMAN 1.395043941 VDAK_ 549.6_258.1 AYSDLSR_ SAMP_HUMAN 1.177349958 406.2_375.2 LIEIANHVDK_ ADA12_HUMAN 1.14243936 384.6_683.4 QINSYVK_ CBG_HUMAN 1.05284482 426.2_496.3 ALEQDLPVNIK_ CNDP1_HUMAN 0.971678206 620.4_798.5 YISPDQLADLYK_ ENOA_HUMAN 0.902293734 713.4_277.2 AVDIPGLEAATP TENA_HUMAN 0.893163413 YR_ 736.9_286.1 SPEAEDPLGVER_ Z512B_HUMAN 0.856551531 649.8_314.1 ILDGGNK_ CXCL5_HUMAN 0.841485153 358.7_603.3 VGVISFAQK_ TFR2_HUMAN 0.835256078 474.8_580.3 YYGYTGAFR_ TRFL_HUMAN 0.831195917 549.3_450.3 YSHYNER_ HABP2_HUMAN 0.814479968 323.5_418.2 FQLPGQK_ PSG1_HUMAN 0.77635168 409.2_276.1 YENYTSSFFIR_ IL12B_HUMAN 0.761241391 713.8_756.4 TEQAAVAR_ FA12_HUMAN 0.73195592 423.2_615.4 SGVDLADSNQK_ VGFR3_HUMAN 0.72504131 567.3_662.3 VLSSIEQK_ 1433S_HUMAN 0.713380314 452.3_691.4 GTYLYNDCPGPGQ TNR1A_HUMAN 0.704248586 DTDCR_ 697.0_666.3 TSYQVYSK_ C163A_HUMAN 0.69026345 488.2_787.4 TLEAQLTPR_ HEP2_HUMAN 0.654641588 514.8_685.4 AEVIWTSSDHQV PD1L1_HUMAN 0.634751081 LSGK_ 586.3_300.2 TAVTANLDIR_ CHL1_HUMAN 0.619871203 537.3_288.2 ITENDIQIALDDAK_ APOB_HUMAN 0.606313398 779.9_632.3 TASDFITK_ GELS_HUMAN 0.593535076 441.7_781.4 SPQAFYR_ REL3_HUMAN 0.592004045 434.7_556.3 NHYTESISVAK_ NEUR1_HUMAN 0.588383911 624.8_415.2 LTTVDIVTLR_ IL2RB_HUMAN 0.587343951 565.8_815.5

TABLE-US-00050 Random Forest All Late Window Variable UniProt_ID MeanDecreaseGini AVYEAVLR_ PEPD_HUMAN 0.437300283 460.8_587.4 AEIEYLEK_ LYAM1_HUMAN 0.371624293 497.8_552.3 AALAAFNAQNNG FETUA_HUMAN 0.304039734 SNFQLEEISR_ 789.1_746.4 TGVAVNKPAEFT FLNA_HUMAN 0.280588526 VDAK_ 549.6_258.1 AVYEAVLR_ PEPD_HUMAN 0.266788699 460.8_750.4 AYSDLSR_ SAMP_HUMAN 0.247412666 406.2_375.2 VPLALFALNR_ PEPD_HUMAN 0.229955358 557.3_620.4 LIEIANHVDK_ ADA12_HUMAN 0.218186524 384.6_683.4 ITQDAQLK_ CBG_HUMAN 0.217646659 458.8_702.4 WSAGLTSSQVD_ CBG_HUMAN 0.213840705 LYIPK_ 883.0_515.3 FGFGGSTDSGPIR_ ADA12_HUMAN 0.212794469 649.3_745.4 LIEIANHVDK_ ADA12_HUMAN 0.208620264 384.6_498.3 QINSYVK_ CBG_HUMAN 0.202054546 426.2_610.3 QINSYVK_ CBG_HUMAN 0.197235139 426.2_496.3 FQLPGQK_ PSG1_HUMAN 0.188311102 409.2_429.2 VFQYIDLHQDEFVQ CNDP1_HUMAN 0.180534913 TLK_ 708.4_375.2 ALEQDLPVNIK_ CNDP1_HUMAN 0.178464358 620.4_798.5 YYGYTGAFR_ TRFL_HUMAN 0.176050092 549.3_450.3 ALFLDALGPPAVTR_ INHA_HUMAN 0.171492975 720.9_640.4 FQLPGQK_ PSG1_HUMAN 0.167576198 409.2_276.1 SETEIHQGFQHL CBG_HUMAN 0.162231844 HQLFAK_ 717.4_447.2 ALEQDLPVNIK_ CNDP1_HUMAN 0.162165399 620.4_570.4 VPSHAVVAR_ TRFL_HUMAN 0.156742065 312.5_515.3 AVDIPGLEAATPYR_ TENA_HUMAN 0.153681405 736.9_286.1 FTFTLHLETPKPS PSG1_HUMAN 0.152042057 ISSSNLNPR_ 829.4_874.4 VGVISFAQK_ TFR2_HUMAN 0.149034355 474.8_580.3 TLPFSR_ LYAM1_HUMAN 0.143223501 360.7_506.3 SLDFTELDVAAEK_ ANGT_HUMAN 0.141216186 719.4_874.5 SPEAEDPLGVER_ Z512B_HUMAN 0.139843479 649.8_314.1 YGIEEHGK_ CXA1_HUMAN 0.135236953 311.5_341.2

TABLE-US-00051 TABLE 50 Selected Transitions for Early Window Transition Parent Protein LIQDAVTGLTVNGQI ITIH3_HUMAN TGDK_ 972.0_798.4 VQTAHFK_ CO8A_HUMAN 277.5_431.2 FLNWIK_ HABP2_HUMAN 410.7_560.3 ITGFLKPGK_ LBP_HUMAN 320.9_429.3 ALNHLPLEYNSALYSR_ CO6_HUMAN 621.0_538.3 TYLHTYESEI_ ENPP2_HUMAN 628.3_908.4 LIENGYFHPVK_ F13B_HUMAN 439.6_627.4 AVLH1GEK_ THBG_HUMAN 289.5_292.2 QALEEFQK_ CO8B_HUMAN 496.8_680.3 TEFLSNYLTNVDDITL ENPP2_HUMAN VPGTLGR_ 846.8_600.3 TASDFITK_ GELS_HUMAN 441.7_781.4 LPNNVLQEK_ AFAM_HUMAN 527.8_844.5 AHYDLR_ FETUA_HUMAN 387.7_288.2 ITLPDFTGDLR_ LBP_HUMAN 624.3_288.2 IEGNLIFDPNNYLPK_ APOB_HUMAN 874.0_414.2 ITGFLKPGK_ LBP_HUMAN 320.9_301.2 FSVVYAK_ FETUA_HUMAN 407.2_381.2 ITGFLKPGK_ LBP_HUMAN 320.9_429.3 VFQFLEK_ CO5_HUMAN 455.8_811.4 LIQDAVTGLTVNGQI ITIH3_HUMAN TGDK_ 972.0_798.4 DADPDTFFAK_ AFAM_HUMAN 563.8_825.4

TABLE-US-00052 TABLE 51 Selected Proteins for Early Window Protein complement component C6 precursor CO6_HUMAN inter-alpha-trypsin inhibitor heavy chain H3 ITIH3_HUMAN preproprotein Coagulation factor XIII B chain F13B_HUMAN Ectonucleotide pyrophosphatase/phosphodiesterase ENPP2_HUMAN family member 2 Complement component C8 beta chain CO8B_HUMAN thyroxine-binding globulin precursor THBG_HUMAN Hyaluronan-binding protein 2 HABP2_HUMAN lipopolysaccharide-binding protein LBP_HUMAN Complement factor B CFAB_HUMAN Gelsolin GELS_HUMAN afamin precursor AFAM_HUMAN apolipoprotein B-100 precursor APOB_HUMAN complement component C5 CO5_HUMAN Alpha-2-HS-glycoprotein FETUA_HUMAN complement component C8 gamma chain CO8G_HUMAN

TABLE-US-00053 TABLE 52 Selected Transitions for Middle-Late Window Transition Patent Protein VPLALFALNR_ PEPD_HUMAN 557.3_620.4 VFQFLEK_ CO5_HUMAN 455.8_811.4 AQPVQVAEGSEPDGF GELS_HUMAN WEALGGK_ 758.0_574.3 LIEIANHVDK_ ADA12_HUMAN 384.6_498.3 TLAFVR_ FA7_HUMAN 353.7_492.3 ALNHLPLEYNSALYSR_ CO6_HUMAN 621.0_696.4 AVYEAVLR_ PEPD_HUMAN 460.8_587.4 SEPRPGVLLR_ FA7_HUMAN 375.2_654.4 TYLHTYESEI_ ENPP2_HUMAN 628.3_515.3 ALNHLPLEYNSALYSR_ CO6_HUMAN 621.0_538.3

TABLE-US-00054 TABLE 53 Selected Proteins for Middle-Late Window Protein Xaa-Pro dipeptidase PEPD_HUMAN Leucyl-cystinyl aminopeptidase LCAP_HUMAN complement component C5 CO5__HUMAN Gelsolin GELS_HUMAN complement component C6 precursor CO6_HUMAN Endoglin precursor EGLN_HUMAN EGF-containing fibulin-like extracellular matrix FBLN3_HUMAN protein 1 coagulation factor VII isoform a FA7_HUMAN Disintegrin and metalloproteinase domain-containing ADA12_HUMAN protein 12 vitamin D-binding protein isoform 1 precursor VTDB_HUMAN coagulation factor XII precursor FA12_HUMAN Corticosteroid-binding globulin CBG_HUMAN

Example 6. Study V to Further Refine Preterm Birth Biomarkers

[0186] A additional hypothesis-dependent discovery study was performed with a further refined scheduled MRM assay. Less robust transitions were again removed to improve analytical performance and make room for the inclusion of stable-isotope labeled standards (SIS) corresponding to 79 analytes of interest identified in previous studies. SIS peptides have identical amino acid sequence, chromatographic and MS fragmentation behaviour as their endogenous peptide counterparts, but differ in mass. Therefore they can be used to reduce LC-MS analytical variability and confirm analyte identity. Samples included approximately 60 spontaneous PTB cases (delivery at less than 37 weeks, 0 days), and 180 term controls (delivery at greater than or equal to 37 weeks, 0 days). Each case was designated a “matched” control to within one day of blood draw and two “random” controls matched to the same 3 week blood draw window (17-19, 20-22 or 23-25 weeks gestation). For the purposes of analysis these three blood draw windows were combined. Samples were processed essentially as described previously, except that in this study, tryptic digests were reconstituted in a solution containing SIS standards. Raw analyte peak areas were Box-Cox transformed, corrected for run order and batch effects by regression and used for univariate and multivariate statistical analyses. Univariate analysis included determination of p-values for adjusted peak areas for all analytes from t-tests considering cases vs controls defined as either deliveries at >37 weeks (Table 54) or deliveries at >40 weeks (Table 55). Univariate analysis also included the determination of p-values for a linear model that evaluates the dependence of each analyte's adjusted peak area on the time to birth (gestational age at birth minus the gestational age at blood draw) (Table 56) and the gestational age at birth (Table 57). Additionally raw peak area ratios were calculated for endogenous analytes and their corresponding SIS counterparts, Box-Cox transformed and then used for univariate and multivariate statistical analyses. The above univariate analysis was repeated for analyte/SIS peak area ratio values, summarized in Tables 58-61, respectively.

[0187] Multivariate random forest regression models were built using analyte values and clinical variables (e.g. Maternal age, (MAGE), Body mass index, (BMI)) to predict Gestational Age at Birth (GAB). The accuracy of the random forest was evaluated with respect to correlation of the predicted and actual GAB, and with respect to the mean absolute deviation (MAD) of the predicted from actual GAB. The accuracy was further evaluated by determining the area under the receiver operating characteristic curve (AUC) when using the predicted GAB as a quantitative variable to classify subjects as full term or pre-term. Random Forest Importance Values were fit to an Empirical Cumulative Distribution Function and probabilities (P) were calculated. We report the analytes by importance ranking (P>0.7) in the random forest models, using adjusted analyte peak area values (Table 62) and analyte/SIS peak area ratio values (Table 63).

[0188] The probability of pre-term birth, p(PTB), may be estimated using the predicted gestational age at birth (GAB) as follows. The estimate will be based on women enrolled in the Sera PAPR clinical trial, which provided the subjects used to develop the PTB prediction methods.

[0189] Among women with a predicted GAB of j days plus or minus k days, p(PTB) was estimated as the proportion of women in the PAPR clinical trial with a predicted GAB of j days plus or minus k days who actually deliver before 37 weeks gestational age.

[0190] More generally, for women with a predicted GAB of j days plus or minus k days, the probability that the actual gestational age at birth will be less than a specified gestational age, p(actual GAB<specified GAB), was estimated as the proportion of women in the PAPR clinical trial with a predicted GAB of j days plus or minus k days who actually deliver before the specified gestational age. FIG. 1 depicts a scatterplot of actual gestational age at birth versus predicted gestational age from random forest regression model. FIG. 2 shows the distribution of predicted gestational age from random forest regression model versus actual gestational age at birth (GAB), where actual GAB was given in categories of (i) less than 37 weeks, (ii) 37 to 39 weeks, and (iii) 40 weeks or greater.

TABLE-US-00055 TABLE 54 Univariate p-values for Ad_usted Peak Areas (<37 vs >37 weeks) Transition Protein pvalue SPELQAEAK_ APOA2_HUMAN 0.00246566 486.8_659.4 ALALPPLGLAPLLNLW SHBG_HUMAN 0.002623332 AKPQGR_ 770.5_457.3 ALALPPLGLAPLLNLW SHBG_HUMAN 0.002822593 AKPQGR_ 770.5_256.2 SPELQAEAK_ APOA2_HUMAN 0.003183869 486.8_788.4 VVLSSGSGPGLDLPLVL SHBG_HUMAN 0.004936049 GLPLQLK_ 791.5_768.5 VVLSSGSGPGLDLPLVL SHBG_HUMAN 0.005598977 GLPLQLK_ 791.5_598.4 DYWSTVK_ APOC3_HUMAN 0.005680405 449.7_347.2 DYWSTVK_ APOC3_HUMAN 0.006288693 449.7_620.3 WGAAPYR_ PGRP2_HUMAN 0.006505238 410.7_634.3 DALSSVQESQVAQQAR_ APOC3_HUMAN 0.007626246 573.0_502.3 DALSSVQESQVAQQAR_ APOC3_HUMAN 0.008149335 573.0_672.4 LSIPQITTK_ PSG5_HUMAN 0.009943955 500.8_687.4 GWVTDGFSSLK_ APOC3_HUMAN 0.010175055 598.8_854.4 IALGGLLFPASNLR_ SHBG_HUMAN 0.010784167 481.3_657_4 AKPALEDLR_ APOA1_HUMAN 0.011331968 506.8_813.5 WGAAPYR_ PGRP2_HUMAN 0.011761088 410.7_577.3 VPLALFALNR_ PEPD_HUMAN 0.014050395 557.3_620.4 FSLVSGWGQLLDR_ FA7_HUMAN 0.014271151 493.3_447.3 LSIPQITTK_ PSG5_HUMAN 0.014339942 500.8_800.5 TLAFVR_ FA7_HUMAN 0.014459876 353.7_274_2 DVLLLVHNLPQNLPGY PSG9_HUMAN 0.016720007 FWYK_ 810.4_960.5 FSVVYAK_ FETUA_HUMAN 0.016792786 407.2_381.2 DVLLLVHNLPQNLPGY PSG9_HUMAN 0.017335929 FWYK_ 810.4_215.1 SEPRPGVLLR_ FA7_HUMAN 0.018147773 375.2_654.4 ALNHLPLEYNSALYSR_ CO6_HUMAN 0.019056484 621.0_538.3 WNFAYWAAHQPWSR_ PRG2_HUMAN 0.019190043 607.3_545.3 ALNHLPLEYNSALYSR_ CO6_HUMAN 0.020218682 621.0_696.4 AQPVQVAEGSEPDGFW GELS_HUMAN 0.020226218 EALGGK_ 758.0_623.4 GWVTDGFSSLK_ APOC3_HUMAN 0.023192703 598.8_953.5 IALGGLLFPASNLR_ SHBG_HUMAN 0.02391691 481.3_412.3 WNFAYWAAHQPWSR_ PRG2_HUMAN 0.026026975 607.3_673.3 FGFGGSTDSGPIR_ ADA12_HUMAN 0.027731407 649.3_745.4 SEYGAALAWEK_ CO6_HUMAN 0.031865281 612.8_788.4 DADPDTFFAK_ AFAM_HUMAN 0.0335897 563.8_302.1 LFIPQITR_ PSG9_HUMAN 0.034140767 494.3_614.4 DVLLLVHNLPQNLP PSG9_HUMAN 0.034653304 GYFWYK_ 810.4_328.2 TLAFVR_ FA7_HUMAN 0.036441189 353.7_492.3 AVLHIGEK_ THBG_HUMAN 0.038539433 289.5_292.2 IHPSYTNYR_ PSG2_HUMAN 0.039733019 384.2_452.2 AGLLRPDYALLGHR_ PGRP2_HUMAN 0.040916226 518.0_369.2 ILILPSVTR_ PSGx_HUMAN 0.042460036 506.3_559.3 YYLQGAK_ ITIH4_HUMAN 0.044511962 421.7_516.3 TPSAAYLWVGTGAS GELS_HUMAN 0.046362381 EAEK_ 919.5_849.4 AGLLRPDYALLGHR_ PGRP2_HUMAN 0.046572355 518.0_595.4 TYLHTYESEI_ ENPP2_HUMAN 0.04754503 628.3_908.4 FSLVSGWGQLLDR_ FA7_HUMAN 0.048642964 493.3_403_2 VNFTEIQK_ FETA_HUMAN 0.04871392 489.8_765.4 LFIPQITR_ PSG9_HUMAN 0.040288923 494.3_727.4 DISEVVTPR_ CFAB_HUMAN 0.049458374 508.3_787.4 SEPRPGVLLR_ FA7_HUMAN 0.049567047 375.2_454_3

TABLE-US-00056 Univariate p-values for Ad_usted Peak Areas (<37 vs >40 weeks) Transition Protein pvalue SPELQAEAK_ APOA2_HUMAN 0.001457796 486.8_659.4 DYWSTVK_ APOC3_HUMAN 0.001619622 449.7_347.2 DYWSTVK_ APOC3_HUMAN 0.002068704 449.7_620.3 DALSSVQESQVAQQAR_ APOC3_HUMAN 0.00250563 573.0_502.3 GWVTDGFSSLK_ APOC3_HUMAN 0.002543943 598.8_854.4 SPELQAEAK_ APOA2_HUMAN 0.003108814 486.8_788.4 SEPRPGVLLR_ FA7_HUMAN 0.004035832 375.2_654.4 DALSSVQESQVAQQAR_ APOC3_HUMAN 0.00434652 573.0_672.4 SEYGAALAWEK_ CO6_HUMAN 0.005306924 612.8_788.4 GWVTDGFSSLK_ APOC3_HUMAN 0.005685534 598.8_953.5 ALNHLPLEYNSALYSR_ CO6_HUMAN 0.005770384 621.0_696.4 TYLHTYESEI_ ENPP2_HUMAN 0.005798991 628.3_515.3 ENPAVIDFELAPIVDLVR_ CO6_HUMAN 0.006248095 670.7_601.4 ALNHLPLEYNSALYSR_ CO6_HUMAN 0.006735817 621.0_538.3 TYLHTYESEI_ ENPP2_HUMAN 0.007351774 628.3_908.4 AGLLRPDYALLGF1R_ PGRP2_HUMAN 0.009541521 518_0_369.2 AKPALEDLR_ APOA1_HUMAN 0.009780371 506.8_813.5 SEYGAALAWEK_ CO6_HUMAN 0.010085363 612.8_845.5 FSLVSGWGQLLDR_ FA7_HUMAN 0.010401836 493.3_447.3 WGAAPYR_ PGRP2_HUMAN 0.011233623 410.7_634.3 ENPAVIDFELAPIVDLVR_ CO6_HUMAN 0.012029564 670.7_811.5 DVLLLVHNLPQNLPGYFWYK_ PSG9_HUMAN 0.014808277 810_4_215.1 LFIPQITR_ PSG9_HUMAN 0.015879755 494.3_614.4 WGAAPYR_ PGRP2_HUMAN 0.016562435 410.7_577.3 AGLLRPDYALLGHR_ PGRP2_HUMAN 0.016793521 518_0_595.4 TLAFVR_ FA7_HUMAN 0.016919708 353.7_492.3 FSLVSGWGQLLDR_ FA7_HUMAN 0.016937583 493.3_403.2 WWGGQPLWITATK_ ENPP2_HUMAN 0.019050115 772.4_373.2 GYVIIKPLVWV_ SAMP_HUMAN 0.019675317 643.9_304.2 DVLLLVHNLPQNLPG PSG9_HUMAN 0.020387647 YFWYK_ 810.4_960.5 FGFGGSTDSGPIR_ ADA12_HUMAN 0.020458335 649.3_745.4 DVLLLVHNLPQNLP PSG9_HUMAN 0.021488084 GYFWYK_ 810.4_328.2 WWGGQPLWITATK_ ENPP2_HUMAN 0.021709354 772.4_929.5 LDFHFSSDR_ INHBC_HUMAN 0.022403383 375.2_448.2 LFIPQITR_ PSG9_HUMAN 0.025561103 494.3_727.4 TEFLSNYLTNVDDI ENPP2_HUMAN 0.029344366 TLVPGTLGR_ 846.8_600.3 LSIPQITTK_ PSG5_HUMAN 0.031361776 500.8_800.5 ALVLELAK_ INHBE_HUMAN 0.031690737 428.8_672.4 SEPRPGVLLR_ FA7_HUMAN 0.033067953 375.2_454.3 LSIPQITTK_ PSG5_HUMAN 0.033972449 500.8_687.4 LDFHFSSDR_ INHBC_HUMAN 0.034500249 375.2_611.3 LDFHFSSDR_ INHBC_HUMAN 0.035166664 375.2_464.2 GAVHVVVAETDYQS CO8G_HUMAN 0.037334975 FAVLYLER_ 822.8_580.3 HELTDEELQSLFTN AFAM_HUMAN 0.039258528 FANVVDK_ 817.1_854_4 AYSDLSR_ SAMP_HUMAN 0.04036485 406.2_375.2 YYLQGAK_ ITIH4_HUMAN 0.042204165 421.7_516.3 ILPSVPK_ PGH1_HUMAN 0.042397885 377.2_264.2 ELLESYIDGR_ THRB_HUMAN 0.043053589 597.8_710.4 ALALPPLGLAPLLN SHBG_HUMAN 0.045692283 LWAKPQGR_ 770.5_256.2 VGEYSLYIGR_ SAMP_HUMAN 0.04765767 578.8_871.5 ANDQYLTAAALHNL ILIA_HUMAN 0.048928376 DEAVK_ 686.4_317.2 YYGYTGAFR_ TRFL_HUMAN 0.049568351 549.3_551.3

TABLE-US-00057 TABLE 56 Univariate p-values for Adjusted Peak Areas in Time to Birth Linear Model Protein pvalue ADA12_HUMAN 0.003412707 ENPP2_HUMAN 0.003767393 ADA12_HUMAN 0.004194234 ENPP2_HUMAN 0.004298493 ADA12_HUMAN 0.004627197 ADA12_HUMAN 0.004918852 ENPP2_HUMAN 0.005792374 CO6_HUMAN 0.005858282 ENPP2_HUMAN 0.007123606 CO6_HUMAN 0.007162317 ENPP2_HUMAN 0.008228726 ENPP2_HUMAN 0.009168492 PSG9_HUMAN 0.011531192 PSG9_HUMAN 0.019389627 PSG9_HUMAN 0.023680865 INHBE_HUMAN 0.02581564 B2MG_HUMAN 0.026544689 LBP_HUMAN 0.031068274 PSG9_HUMAN 0.031091843 APOA2_HUMAN 0.033130498 INHBC_HUMAN 0.03395215 CBG_HUMAN 0.034710348 PSGx_HUMAN 0.035719227 CBG_HUMAN 0.036331871 CSH_HUMAN 0.039896611 CSH_HUMAN 0.04244001 SAMP_HUMAN 0.047112128 LBP_HUMAN 0.048141371 LBP_HUMAN 0.048433174 CO6_HUMAN 0.04850949 PSGx_HUMAN 0.049640167

TABLE-US-00058 TABLE 57 Univariate p-values for Ad_usted Peak Areas  in Gestation Age at Birth Linear Model Transition Protein pvalue ENPAVIDFELAPIVDLVR_ CO6_HUMAN 0.000117239 670.7_811.5 ENPAVIDFELAPIVDLVR_ CO6_HUMAN 0.000130113 670.7_601.4 TYLHTYESEI_ ENPP2_HUMAN 0.000160472 628.3_908.4 TYLHTYESEI_ ENPP2_HUMAN 0.000175167 628.3_515.3 TEFLSNYLTNVDDITLV ENPP2_HUMAN 0.000219886 PGTLGR_ 846.8_600.3 TEFLSNYLTNVDDITLV ENPP2_HUMAN 0.000328416 PGTLGR_ 846.8_699.4 WWGGQPLWITATK_ ENPP2_HUMAN 0.000354644 772.4_373.2 WWGGQPLWITATK_ ENPP2_HUMAN 0.000390821 772.4_929.5 SEYGAALAWEK_ CO6_HUMAN 0.000511882 612_8_788.4 LDFHFSSDR_ INHBC_HUMAN 0.000600637 375.2_448.2 ALVLELAK_ INHBE_HUMAN 0.000732445 428.8_672.4 GLQYAAQEGLLALQSE LBP_HUMAN 0.000743924 LLR_ 1037_1_929_5 DVLLLVHNLPQNLPGY PSG9_HUMAN 0.000759173 FWYK_ 810.4_960.5 FGFGGSTDSGPIR_ ADA12_HUMAN 0.001224347 649.3_745.4 DVLLLVHNLPQNLPGY PSG9_HUMAN 0.001241526 FWYK_ 810.4_328.2 GYVIIKPLVWV_ SAMP_HUMAN 0.001853785 643.9_304.2 SPELQAEAK_ APOA2_HUMAN 0.001856303 486.8_659.4 GLQYAAQEGLLALQSE LBP_HUMAN 0.001978165 LLR_ 1037.1_858_5 LDFHFSSDR_ INHBC_HUMAN 0.002098948 375.2_61_F3 LIEIANHVDK_ ADA12_HUMAN 0.002212096 384.6_683.4 SFRPFVPR_ LBP_HUMAN 0.002545286 335.9_272.2 SFRPFVPR_ LBP_HUMAN 0.002620268 335.9_635.3 WSAGLTSSQVDLYIPK_ CBG_HUMAN 0.002787272 883.0_515_3 DLHLSDVFLK_ CO6_HUMAN 0.002954612 396.2_260.2 LIEIANHVDK_ ADA12_HUMAN 0.002955081 384.6_498.3 DVLLLVHNLPQNLPG PSG9_HUMAN 0.003541011 YFWYK_ 810.4_215.1 LFIPQITR_ PSG9_HUMAN 0.003750666 494.3_614.4 FGFGGSTDSGPIR_ ADA12_HUMAN 0.003773696 649.3_946.5 YYLQGAK_ ITIH4_HUMAN 0.004064026 421.7_516.3 SEYGAALAWEK_ CO6_HUMAN 0.004208136 612.8_845.5 AITPPHPASQANIIF FBLN1_HUMAN 0.004709104 DITEGNLR_ 825.8_459.3 LDFHFSSDR_ INHBC_HUMAN 0.005355741 375.2_464.2 HELTDEELQSLFTNFA AFAM_HUMAN 0.005370567 NVVDK_ 817.1_854.4 ALNHLPLEYNSALYSR_ CO6_HUMAN 0.005705922 621.0_696.4 ITQDAQLK_ CBG_HUMAN 0.006762484 458.8_702.4 ITLPDFTGDLR_ LBP_HUMAN 0.006993268 624.3_920.5 SILFLGK_ THBG_HUMAN 0.007134146 389.2_577.4 WSAGLTSSQVDLYIPK_ CBG_HUMAN 0.007670388 883.0_357.2 GVTSVSQIFHSPDLAIR_ IC1_HUMAN 0.007742729 609.7_472.3 VGEYSLYIGR_ SAMP_HUMAN 0.007778691 578.8_871.5 ITLPDFTGDLR_ LBP_HUMAN 0.008179918 624_3_288_2 YYLQGAK_ ITIH4_HUMAN 0.008404686 421.7_327.1 ALNHLPLEYNSALYSR_ CO6_HUMAN 0.008601162 621.0_538_3 DYWSTVK_ APOC3_HUMAN 0.008626786 449.7_620.3 TVQAVLTVPK_ PEDF_HUMAN 0.008907523 528.3_855.5 ITGFLKPGK_ LBP_HUMAN 0.009155417 320.9_301.2 LFIPQITR_ PSG9_HUMAN 0.009571006 494.3_727.4 SPELQAEAK_ APOA2_HUMAN 0.009776508 486.8_788.4 DYWSTVK_ APOC3_HUMAN 0.00998356 449.7_347.2 ITGFLKPGK_ LBP_HUMAN 0.010050264 320.9_429.3 FLNWIK_ HABP2_HUMAN 0.010372454 410.7_560.3 DLHLSDVFLK_ CO6_HUMAN 0.010806378 396.2_366.2 GVTSVSQIFHSPDLAIR_ IC1_HUMAN 0.011035991 609.7_908.5 VEHSDLSFSK_ B2MG_HUMAN 0.011113172 383.5_468.2 LLDSLPSDTR_ IC1_HUMAN 0.011589013 558.8_276.2 LLDSLPSDTR_ IC1_HUMAN 0.011629438 558.8_890.4 QALEEFQK_ CO8B_HUMAN 0.011693839 496.8_551.3 LLDSLPSDTR_ IC1_HUMAN 0.012159314 558.8_575.3 IIGGSDADIK_ C1S_HUMAN 0.013080243 494.8_762.4 AFIQLWAFDAVK_ AMBP_HUMAN 0.013462234 704.9_650.4 GFQALGDAADIR_ TIMP1_HUMAN 0.014370997 617.3_717_4 LPNNVLQEK_ AFAM_HUMAN 0.014424891 527.8_730.4 DTDTGALLFIGK_ PEDF_HUMAN 0.014967952 625_8_217.1 VQTAHFK_ CO8A_HUMAN 0.01524844 277.5_502.3 ILILPSVTR_ PSG1_HUMAN 0.015263132 506.3_559.3 SILFLGK_ THBG_HUMAN 0.015265233 389.2_201.1 TVQAVLTVPK_ PEDF_HUMAN 0.015344052 528.3_428.3 VEPLYELVTATDFAYSSTVR_ CO8B_HUMAN 0.015451068 754.4_712.4 FSLVSGWGQLLDR_ FA7_HUMAN 0.015510454 493.3_447_3 GWVTDGFSSLK_ APOC3_HUMAN 0.01610797 598.8_854.4 LSETNR_ PSG1_HUMAN 0.016433362 360.2_519.3 TQILEWAAER_ EGLN_HUMAN 0.01644844 608.8_632.3 SETEIHQGFQHLHQLFAK_ CBG_HUMAN 0.016720367 717_4_318.1 TNLESILSYPK_ IC1_HUMAN 0.017314185 632.8_936.5 TNLESILSYPK_ IC1_HUMAN 0.017593786 632.8_807.5 AYSDLSR_ SAMP_HUMAN 0.018531348 406.2_375.2 YEVQGEVFTKPQLWP_ CRP_HUMAN 0.019111323 911_0_392.2 AYSDLSR_ SAMP_HUMAN 0.019271266 406.2_577.3 QALEEFQK_ CO8B_HUMAN 0.019429489 496.8_680.3 APLTKPLK_ CRP_HUMAN 0.020110081 289.9_398.8 FQPTLLTLPR_ IC1_HUMAN 0.020114306 593.4_276.1 ITQDAQLK_ CBG_HUMAN 0.020401782 453.8_803.4 AVLH1GEK_ THBG_HUMAN 0.02056597 289.5_292.2 ANDQYLTAAALHNLDE ILIA_HUMAN 0.020770124 AVK_ 686.4_317.2 VGEYSLYIGR_ SAMP_HUMAN 0.021126414 578.8_708.4 TLYSSSPR_ IC1_HUMAN 0.021306106 455.7_533.3 VEHSDLSFSK_ B2MG_HUMAN 0.021640643 383.5_234.1 HELTDEELQSLFTNFA AFAM_HUMAN 0.021921609 NVVDK_ 817.1_906.5 TLYSSSPR_ IC1_HUMAN 0.022196181 455.7_696.3 GYVIIKPLVWV_ SAMP_HUMAN 0.023126336 643.9_854.6 DEIPHNDIALLK_ HABP2_HUMAN 0.023232158 459.9_260.2 ILILPSVTR_ PSGx_HUMAN 0.023519909 506.3_785.5 WNFAYWAAHQPWSR_ PRG2_HUMAN 0.023697087 607.3_545.3 FQPTLLTLPR_ IC1_HUMAN 0.023751959 593.4_712.5 AQPVQVAEGSEPDGF GELS_HUMAN 0.024262721 WEALGGK_ 758.0_623.4 DEIPHNDIALLK_ HABP2_HUMAN 0.024414348 459.9_510.8 GDSGGAFAVQDPNDK_ C1S_HUMAN 0.025075028 739.3_716.3 FLNWIK_ HABP2_HUMAN 0.025649617 410.7_561.3 APLTKPLK_ CRP_HUMAN 0.025961162 289.9_357.2 ALDLSLK_ ITIH3_HUMAN 0.026233504 380.2_185.1 GWVTDGFSSLK_ APOC3_HUMAN 0.026291884 598_8_953.5 SETEIHQGFQHLHQLFAK_ CBG_HUMAN 0.026457136 717.4_447.2 GDSGGAFAVQDPNDK_ C1S_HUMAN 0.02727457 739.3_473.2 YEVQGEVFTKPQLWP_ CRP_HUMAN 0.028244448 911.0_293.1 HVVQLR_ IL6RA_HUMAN 0.028428028 376.2_614.4 DTDTGALLFIGK_ PEDF_HUMAN 0.028773557 625.8_818.5 EVPLSALTN1LSAQLI PAI1_HUMAN 0.029150774 SHWK_ 740.8_996.6 AFTECCVVASQLR_ CO5_HUMAN 0.029993325 770.9_574.3 TLAFVR_ FA7_HUMAN 0.030064307 353.7_492.3 LWAYLTIQELLAK_ ITIH1_HUMAN 0.030368674 781.5_300.2 DEIPHNDIALLK_ HABP2_HUMAN 0.031972082 459_9_245_1 AGLLRPDYALLGHR_ PGRP2_HUMAN 0.032057409 518.0_369.2 AVYEAVLR_ PEPD_HUMAN 0.032527521 460.8_587.4 LPNNVLQEK_ AFAM_HUMAN 0.033807082 527.8_844.5 GAVHVVVAETDYQSFA CO8G_HUMAN 0.054370139 VLYLER_ 822_8_580.3 WNFAYWAAHQPWSR_ PRG2_HUMAN 0.0349737 607.3_673.3 EAQLPVIENK_ PLMN_HUMAN 0.035304322 570.8_329.2 VQEAHLTEDQIFYFPK_ CO8G_HUMAN 0.035704382 655.7_701.4 AFIQLWAFDAVK_ AMBP_HUMAN 0.035914532 704.9_836.4 SGFSFGFK_ CO8B_HUMAN 0.037168221 438.7_585.3 SGFSFGFK_ CO8B_HUMAN 0.040182596 438.7_732.4 DADPDTFFAK_ AFAM_HUMAN 0.041439744 563.8_302.1 EAQLPV1ENK_ PLMN_HUMAN 0.041447675 570.8_699.4 IIGGSDADIK_ C1S_HUMAN 0.041683256 494.8_260.2 AVLT1DEK_ A1AT_HUMAN 0.043221658 444.8_718.4 SEPRPGVLLR_ FA7_HUMAN 0.044079127 375.2_654.4 YHFEALADTGISSEFY CO8A_HUMAN 0.045313634 DNANDLLSK_ 940.8_874.5 HFQNLGK_ AFAM_HUMAN 0.047118971 422.2_527.2 LEQGENVFLQATDK_ C1QB_HUMAN 0.047818928 796.4_822.4 NTVISVNPSTK_ VCAM1_HUMAN 0.048102262 580.3_732.4 YYGYTGAFR_ TRFL_HUMAN 0.048331316 549.3_551.3 ISLLLIESWLEPVR_ CSH_HUMAN 0.049561581 834.5_500.3 LQVLGK_ A2GL_HUMAN 0.049738493 329.2_416.3

TABLE-US-00059 TABLE 58 Univariate p-values for Peak Area Ratios (<37 vs >37 weeks) UniProt_ID Transition pvalue SHBG_HUMAN IALGGLLFPASN 0.006134652 LR_ 481.3_ 657.4 SHBG_HUMAN IALGGLLFPASN 0.019049498 LR_ 481.3_ 412.3 APOC3_HUMAN DALSSVQESQVAQ 0.020688543 QAR_ 573.0_ 672.4 THBG_HUMAN AVLH1GEK_ 0.0291698 289.5_ 292.2 PSG9_HUMAN DVLLLVHNLPQNL 0.033518454 PGYFWYK_ 810.4_ 960.5 APOC3_HUMAN DALSSVQESQVAQ 0.043103265 QAR_ 573.0_ 502.3 PSG9_HUMAN LFIPQITR_ 0.04655948 494.3_ 614.4

TABLE-US-00060 TABLE 59 Univariate p-values for Peak Area Ratios (<37 vs >40 weeks) UniProt_ID Transition pvalue APOC3_ DALSSVQESQVA 0.011174438 HUMAN QQAR_573.0_ 672.4 APOC3_ DALSSVQESQVA 0.015231617 HUMAN QQAR_573.0_ 502.3 PSG9_ LFIPQITR_ 0.018308413 HUMAN 494.3_614.4 PSG9_ LFIPQITR_ 0.027616871 HUMAN 494.3_727.4 PSG9_ DVLLLVHNLPQN 0.028117582 HUMAN LPGYFWYK_ 810.4_960.5 THBG_ AVLHIGEK_ 0.038899107 HUMAN 289.5_292.2 CO6_ ALNHLPLEYNSA 0.040662269 HUMAN LYSR_621.0_ 696.4 ENPP2_ TYLHTYESEI_ 0.044545826 HUMAN 628.3_908.4

TABLE-US-00061 TABLE 60 Univariate p-values for Peak Area Ratios in Time to Birth Linear Model UniProt_ID Transition pvalue ADA12_ FGFGGSTDSGPIR_ 5.85E−27 HUMAN 649.3_946.5 ADA12_ FGFGGSTDSGPIR_ 2.65E−24 HUMAN 649.3_745.4 PSG4_ TLF1FGVTK_ 1.07E−20 HUMAN 513.3_215.1 PSG4_ TLFIFGVTK_ 2.32E−20 HUMAN 513.3_811.5 PSGx_ ILILPSVTR_ 8.25E−16 HUMAN 506.3_785.5 PSGx_ ILILPSVTR_ 9.72E−16 HUMAN 506.3_559.3 PSG1_ FQLPGQK_ 1.29E−12 HUMAN 409.2_429.2 PSG11_ LFIPQITPK_ 2.11E−12 HUMAN 528.8_261.2 PSG1_ FQLPGQK_ 2.33E−12 HUMAN 409.2_276.1 PSG11_ LFIPQITPK_ 3.90E−12 HUMAN 528.8_683_4 PSG6_ SNPVTLNVLY 5.71E−12 HUMAN GPDLPR_ 585.7_817.4 PSG6_ SNPVTLNVLY 1.82E−11 HUMAN GPDLPR_ 585.7_654.4 VGFR3_ SGVDLADSNQK_ 4.57E−11 HUMAN 567.3_662.3 INHBE_ ALVLELAK_ 1.04E−08 HUMAN 428.8_331.2 PSG2_ IHPSYTNYR_ 6.27E−08 HUMAN 384.2_452.2 PSG9_ LFIPQITR_ 1.50E−07 HUMAN 494.3_727.4 VGFR3_ SGVDLADSNQK_ 2.09E−07 HUMAN 567.3_591.3 PSG9_ LFIPQITR_ 2.71E−07 HUMAN 494.3_614_4 PSG9_ DVLLLVHNLPQ 3.10E−07 HUMAN NLPGYFWYK_ 810.4_960.5 PSG2_ IHPSYTNYR_ 2.55E−06 HUMAN 384.2_338.2 ITIH3_ LIQDAVTGLTV 2.76E−06 HUMAN NGQITGDK_ 972.0_640.4 ENPP2_ TYLHTYESEI_ 2.82E−06 HUMAN 628.3_908_4 ENPP2_ WWGGQPLWI 3.75E−06 HUMAN TATK_ 772.4_373.2 PSG9_ DVLLLVHNLPQ 3.94E−06 HUMAN NLPGYFWYK_ 810.4_328.2 B2MG_ VEHSDLSFSK_ 5.42E−06 HUMAN 383.5_468.2 ENPP2_ WWGGQPLW 7.93E−06 HUMAN ITATK_ 772.4_929.5 ANGT_ ALQDQLV 1.04E−05 HUMAN LVAAK_ 634.9_289.2 B2MG_ VNHVTLSQPK_ 1.46E−05 HUMAN 374.9_244.2 AFAM_ LPNNVLQEK_ 1.50E−05 HUMAN 527.8_730.4 AFAM_ LPNNVLQEK_ 1.98E−05 HUMAN 527.8_844.5 THBG_ AVLHIGEK_ 2.15E−05 HUMAN 289.5_292.2 ENPP2_ TYLHTYESEI_ 2.17E−05 HUMAN 628.3_515.3 IL12B_ DIIKPDPPK_ 3.31E−05 HUMAN 511.8_342.2 AFAM_ DADPDTFFAK_ 6.16E−05 HUMAN 563.8_302.1 THBG_ AVLHIGEK_ 8.34E−05 HUMAN 289.5_348.7 PSG9_ DVLLLVHNLPQ 0.000104442 HUMAN NLPGYFWYK_ 810.4_215.1 B2MG_ VEHSDLSFSK_ 0.000140786 HUMAN 383.5_234.1 TRFL_ YYGYTGAFR_ 0.000156543 HUMAN 549.3_450.3 HEMO_ QGHNSVFLIK_ 0.000164578 HUMAN 381.6_260.2 A1BG_ LLELTGPK_ 0.000171113 HUMAN 435.8_227.2 CO6_ ALNHLPLEYN 0.000242116 HUMAN SALYSR_ 621.0_696.4 CO6_ ALNHLPLEYN 0.00024681 HUMAN SALYSR_ 621.0_538.3 ALS_ IRPHTFTGLSGLR_ 0.000314359 HUMAN 485.6_432.3 IT1H2_ LSNHNHGlAQR_ 0.0004877 HUMAN 413.5_544_3 PEDF_ TVQAVLTVPK_ 0.000508174 HUMAN 528.3_855.5 AFAM_ HFQNLGK_ 0.000522139 HUMAN 422.2_527.2 FLNA_ TGVAVNKPAEFT 0.000594403 HUMAN VDAK_ 549.6_258.1 ANGT_ ALQDQLVLVAAK_ 0.000640673 HUMAN 634.9_956.6 AFAM_ HFQNLGK_ 0.000718763 HUMAN 422.2_285.1 HGFA_ LHKPGVYTR_ 0.000753293 HUMAN 357.5_692.4 HGFA_ LHKPGVYTR_ 0.000909298 HUMAN 357.5_479.3 HABP2_ FLNWIK_ 0.001282014 HUMAN 410.7_561.3 FETUA_ HTLNQIDEVK_ 0.001389792 HUMAN 598.8_951.5 AFAM_ DADPDIFFAK_ 0.001498237 HUMAN 563.8_825.4 B2MG_ VNHVTLSQPK_ 0.001559862 HUMAN 374.9_459.3 ALS_ IRPHTFTGLSGLR_ 0.001612361 HUMAN 485.6_545.3 A1BG_ LLELTGPK_ 0.002012656 HUMAN 435.8_644.4 F13B_ LIENGYFHPVK_ 0.00275216 HUMAN 439.6_343.2 ITIH2_ LSNENHGIAQR_ 0.00356561 HUMAN 413.5_519.8 APOC3_ DALSSVQESQVA 0.00392745 HUMAN QQAR_573.0_ 672.4 F13B_ LIENGYFHPVK_ 0.00434836 HUMAN 439.6_627.4 PEDF_ TVQAVLTVPK_ 0.00482765 HUMAN 528.3_428.3 PLMN_ YEFLNGR_ 0.007325436 HUMAN 449.7_293.1 HEMO_ QGHNSVFLIK_ 0.009508516 HUMAN 381.6_520.4 FETUA_ HTLNQIDEVK_ 0.010018936 HUMAN 598.8_958.5 CO5_ LQGTLPVEAR_ 0.011140661 HUMAN 542.3_842.5 PLMN_ YEFLNGR_ 0.01135322 HUMAN 449.7_606.3 CO5_ TLLPVSKPE1R_ 0.015045275 HUMAN 418.3_288.2 HABP2_ FLNWIK_ 0.01523134 HUMAN 410.7_560.3 APOC3_ DALSSVQESQVA 0.01584708 HUMAN QQAR_573.0_ 502.3 CO5_ LQGTLPVEAR_ 0.017298064 HUMAN 542.3_571.3 CFAB_ DISEWTPR_ 0.021743221 HUMAN 508.3_472.3 CERU_ TTIEKPVWLG 0.02376225 FLGPIIK_ HUMAN 638.0_640.4 CO8G_ SLPVSDSVLSGFEQR_ 0.041150397 HUMAN 810.9_723.3 CO8G_ FLQEQGHR_ 0.042038143 HUMAN 338.8_497.3 CO5_ VFQFLEK_ 0.043651929 HUMAN 455.8_811.4 CO8B_ QALEEFQK_ 0.04761631 HUMAN 496.8_680.3

TABLE-US-00062 TABLE 61 Univariate p-values for Peak Area Ratios in Gestation Age at Birth Linear Model UniProt_ID Transition pvalue PSG9_ DVLLLVHNLPQNLP 0.000431547 HUMAN GYFWYK_ 810.4_960.5 B2MG_ VEHSDLSFSK_ 0.000561148 HUMAN 383.5_468.2 PSG9_ DVLLLVHNLPQNLP 0.000957509 HUMAN GYFWYK_ 810.4_328.2 ENPP2_ TYLHTYESEI_ 0.001058809 HUMAN 628.3_908.4 THBG_ AVLHIGEK_ 0.001180484 HUMAN 289.5_292.2 ENPP2_ WWGGQPLWITATK_ 0.001524983 HUMAN 772.4_373.2 PSG9_ LFIPQITR_ 0.001542932 HUMAN 494.3_614_4 ENPP2_ WWGGQPLWITATK_ 0.002047607 HUMAN 772.4_929.5 ENPP2_ TYLHTYESEI_ 0.003087492 HUMAN 628.3_515.3 PSG9_ LFIPQITR_ 0.00477154 HUMAN 494.3_727.4 PSG9_ DVLLLVHNLPQ 0.004824351 HUMAN NLPGYFWYK_ 810.4_215.1 THBG_ AVLHIGEK_ 0.006668084 HUMAN 289.5_348.7 AFAM_ LPNNVLQEK_ 0.006877647 HUMAN 527.8_730.4 ADA12_ FGFGGSTDSGPIR_ 0.011738104 HUMAN 649.3_745_4 PEDF_ TVQAVLTVPK_ 0.013349511 HUMAN 528.3_855.5 A1BG_ LLELTGPK_ 0.015793885 HUMAN 435.8_227.2 ITIH3_ ALDLSLK_ 0.016080436 HUMAN 380.2_185.1 ADA12_ FGFGGSTDSGP 0.017037089 HUMAN IR_ 649.3_946.5 B2MG_ VEHSDLSFSK_ 0.017072093 HUMAN 383.5_234.1 CO6_ ALNHLPLEYNS 0.024592775 HUMAN ALYSR_ 621.0_696.4 TRFL_ YYGYTGAFR_ 0.030890831 HUMAN 549.3_450.3 AFAM_ DADPDTFFAK_ 0.033791429 HUMAN 563.8_302.1 CO6_ ALNHLPLEYNS 0.034865341 HUMAN ALYSR_ 621.0_538.3 AFAM_ LPNNVLQEK_ 0.039880594 HUMAN 527.8_844.5 PEDF_ TVQAVLTVPK_ 0.040854402 HUMAN 528.3_428.3 PLMN_ EAQLPVIENK_ 0.041023812 HUMAN 570.8_329.2 LBP_ ITLPDFTGDLR_ 0.042276813 HUMAN 624.3_920.5 CO8G_ VQEAHLTEDQI 0.042353851 HUMAN FYFPK_ 655.7_701.4 PLMN_ YEFLNGR_ 0.04416504 HUMAN 449.7_606.3 B2MG_ VNHVTLSQPK_ 0.045458409 HUMAN 374.9_459.3 CFAB_ DISEVVTPR_ 0.046493405 HUMAN 508.3_472.3 INHBE_ ALVLELAK_ 0.04789353 HUMAN 428.8_331.2

TABLE-US-00063 TABLE 62 Random Forest Importance Values Using Adjusted Peak Areas Transition Rank Importance INHBE_ALVLELAK_428.8_672.4 1 2964.951571 EGLN_TQILEWAAER_608.8_761.4 2 1218.3406 FA7_SEPRPGVLLR_375.2_654.4 3 998.92897 CBG_ITQDAQLK_458.8_702.4 4 930.9931102 ITIH3_ALDLSLK_380.2_185.1 5 869.6315408 ENPP2_WWGGQPLWITATK_772.4_929.5 6 768.9182114 CBG_ITQDAQLK_458.8_803.4 7 767.8940452 PSG1_LSETNR_360.2_519.3 8 714.6160065 CAA60698_LEPLYSASGPGLRPLVIK_637.4_834.5 9 713.4086612 INHBC_LDFHFSSDR_375.2_611.3 11 681.2442909 CBG_QINSYVK_426.2_610.3 12 674.3363415 LBP_GLQYAAQEGLLALQSELLR_1037.1_858.5 13 603.197751 A1BG_LLELTGPK_435.8_644.4 14 600.9902818 CO6_DLHLSDVFLK_396.2_366.2 15 598.8214342 VCAM1_TQIDSPLSGK_523.3_816.5 16 597.4038769 LRP1_NAVVQGLEQPHGLVVHPLR_688.4_285.2 17 532.0500081 CBG_QINSYVK_426.2_496.3 18 516.5575201 CO6_ENPAVIDFELAP1VDLVR_670.7_811.5 19 501.4669261 ADA12_FGFGGSTDSGPIR_649.3_745.4 20 473.5510333 CO6_DLHLSDVFLK_396.2_260.2 21 470.5473702 ENPP2_TYLHTYESEI_628.3_908.4 22 444.7580726 A1BG_LLELTGPK_435.8_227.2 23 444.696292 FRIH_QNYHQDSEAAINR_515.9_544.3 24 439.2648872 ENPP2_TEFLSNYLTNVDDITLVPGTLGR_846_8_600.3 25 389.3769604 CBG_WSAGLTSSQVDLYIPK_883.0_515.3 26 374.0749768 C1QC_FQSVFTVTR_542.8_623.4 27 370.6957977 GELS_DPDQTDGLGLSYLSSHIANVER_796.4_456.2 28 353.1176588 A1BG_ATWSGAVLAGR_544.8_643.4 29 337.4580124 APOA1_AKPALEDLR_506.8_813.5 30 333.5742035 ENPP2_TYLHTYESEI_628.3_515.3 31 322.6339162 PEPD_AVYEAVLR_460.8_750.4 32 321.4377907 TIMP1_GFQALGDAADIR_617.3_717.4 33 310.0997949 ADA12_LIEIANHVDK_384.6_498.3 34 305.8803542 PGRP2_WGAAPYR_410.7_577.3 35 303.5539874 PSG9_LFIPQITR_494.3_614.4 36 300.7877317 HABP2_FLNWIK_410.7_560.3 37 298.3363186 CBG_WSAGLTSSQVDLYIPK_883.0_357.2 38 297.2474385 PSG2_IHPSYTNYR_384.2_452.2 39 292.6203405 PSG5_LSIPQITTK_500.8_800.5 40 290.2023364 HABP2_FLNWIK_410.7_561.3 41 289.5092933 CO6_SEYGAALAWEK_612.8_788.4 42 287.7634114 ADA12_LIEIANHVDK_384.6_683.4 43 286.5047372 EGLN_TQILEWAAER_608.8_632.3 44 284.5138846 CO6_ENPAVIDFELAPIVDLVR_670.7_601.4 45 273.5146272 FA7_FSLVSGWGQLLDR_493.3_447.3 46 271.7850098 ITIH3_ALDLSLK_380.2_575.3 47 269.9425709 ADA12_FGFGGSTDSGPIR_649.3_946.5 48 264.5698225 FETUA_AALAAFNAQNNGSNFQLEEISR_789.1_746.4 49 247.4728828 FBLN1_AITPPHPASQANIIFDITEGNLR_825.8_459.3 50 246.572102 TSP1_FVFGTTPEDILR_697.9_843.5 51 245.0459575 VCAM1_NTVISVNPSTK_580.3_732.4 52 240.576729 ENPP2_TEFLSNYLTNVDDITLVPGTLGR_846.8_699.4 53 240.1949512 FBLN3_ELPQSIVYK_538.8_409.2 55 233.6825304 ACTB_VAPEEHPVLLTEAPLNPK_652.0_892.5 56 226.9772749 TSP1_FVFGTTPEDILR_697.9_742.4 57 224.4627393 PLMN_EAQLPVIENK_570.8_699.4 58 221.4663735 C1S_IIGGSDADEK_494.8_260.2 59 218.069476 ILIA_ANDQYLTAAALHNLDEAVK_686.4_317.2 60 216.5531949 PGRP2_WGAAPYR_410.7_634.3 61 211.0918302 PSG5_LSIPQITTK_500.8_687.4 62 208.7871461 PSG6_SNPVTLNVLYGPDLPR_585.7_654.4 63 207.9294937 PRG2_WNFAYWAAHQPWSR_607.3_545.3 64 202.9494031 CXCL2_CQCLQTLQGIHLK_13p8RT_533.6_567.4 65 202.9051326 CXCL2_CQCLQTLQGIHLK_13p48RT_533.6_695.4 66 202.6561548 G6PE_LLDFEFSSGR_585.8_553.3 67 201.004611 GELS_TASDFITK_441.7_710.4 68 200.2704809 B2MG_VEHSDLSFSK_383.5_468.2 69 199.880987 CO8B_IPGIFELGISSQSDR_809.9_849.4 70 198.7563875 PSG8_LQLSETNR_480.8_606.3 71 197.6739966 LBP_GLQYAAQEGLLALQSELLR_1037.1_929.5 72 197.4094851 AFAM_LPNNVLQEK_527.8_844.5 73 196.8123228 MAGE 74 196.2410502 PSG2_IHPSYTNYR_384.2_338.2 75 196.2410458 PSG9_LFIPQITR_494.3_727.4 76 193.5329266 TFR1_YNSQLLSFVR_613.8_734.5 77 193.2711994 C1R_QRPPDLDTSSNAVDLLFFTDESGDSR_961.5_866.3 78 193.0625419 PGH1_ILPSVPK_377.2_264.2 79 190.0504508 FA7_SEPRPGVLLR_375.2_454.3 80 188.2718422 FA7_TLAFVR_353.7_274.2 81 187.6895294 PGRP2_DGSPDVTTADIGANTPDATK_973.5_844.4 82 185.6017519 C1S_IIGGSDADIK_494.8_762.4 83 184.5985543 PEPD_VPLALFALNR_557.3_620.4 84 184.3962957 C1S_EDTPNSVWEPAK_686.8_630.3 85 179.2043504 CHL1_TAVTANLDIR_537.3_802.4 86 174.9866792 CHL1_VIAVNEVGR_478.8_744.4 88 172.2053147 SDF1_ILNTPNCALQIVAR_791.9_341.2 89 171.4604557 PAI1_EVPLSALTNILSAQLISHWK_740.8_996.6 90 169.5635635 AMBP_AFIQLWAFDAVK_704.9_650.4 91 169.2124477 G6PE_LLDFEFSSGR_585.8_944.4 92 168.2398598 THBG_SILFLGK_389.2_577.4 93 166.3110206 PRDX2_GLFIIDGK_431.8_545.3 94 164.3125132 ENPP2_WWGGQPLWITATK_772.4_373.2 95 163.4011689 VGFR3_SGVDLADSNQK_567.3_662.3 96 162.8822352 C1S_EDTPNSVWEPAK_686.8_315.2 97 161.6140915 AFAM_DADPDTFFAK_563.8_302.1 98 159.5917449 CBG_SETEIHQGFQHLHQLFAK_717.4_447.2 99 156.1357404 C1S_LLEVPEGR_456.8_686.4 100 155.1763293 PTGDS_GPGEDFR_389.2_623.3 101 154.9205208 ITIH2_IYLQPGR_423.7_329.2 102 154.6552717 FA7_TLAFVR_353.7_492.3 103 152.5009422 FA7_FSLVSGWGQLLDR_493.3_403.2 104 151.9971204 SAMP_VGEYSLYIGR_578.8_871.5 105 151.4738449 APOH_EHSSLAFWK_552.8_267.1 106 151.0052645 PGRP2_AGLLRPDYALLGHR_518.0_595.4 107 150.4149907 C1QC_FNAVLTNPQGDYDTSTGK_964.5_333.2 108 149.2592827 PGRP2_AGLLRPDYALLGHR_518.0_369.2 109 147.3609354 PGRP2_TFTLLDPK_467.8_686.4 111 145.2145223 CO5_TDAPDLPEENQAR_728.3_843.4 112 144.5213118 THRB_ELLESYIDGR_597.8_839.4 113 143.924639 GELS_DPDQTDGLGLSYLSSHIANVER_796.4_328.1 114 142.8936101 TRFL_YYGYTGAFR_549.3_450.3 115 142.8651352 HEMO_QGHNSVFLIK_381.6_260.2 116 142.703845 C1S_GDSGGAFAVQDPNDK_739.3_716.3 117 142.2799122 B1A4H9_AHQLAIDTYQEFR_531.3_450.3 118 138.196407 C1S_SSNNPHSPIVEEFQVPYNK_729.4_261.2 119 136.7868935 HYOU1_LPATEKPVLLSK_432.6_347.2 120 136.1146437 FETA_GYQELLEK_490.3_502.3 121 135.2890322 LRP1_SERPPIFEIR_415.2_288.2 122 134.6569527 CO6_SEYGAALAWEK_612.8_845.5 124 132.8634704 CERU_TTIEKPVWLGFLGPIIK_638.0_844.5 125 132.1047746 IBP1_AQETSGEEISK_589.8_850.4 126 130.934446 SHBG_VVLSSGSGPGLDLPLVLGLPLQLK_791.5_768.5 127 128.2052287 CBG_SETEIHQGFQHLHQLFAK_717.4_318.1 128 127.9873837 A1AT_LSITGTYDLK_555.8_696.4 129 127.658818 PGRP2_DGSPDVTTADIGANTPDATK_973.5_531.3 130 126.5775806 C1QB_LEQGENVFLQATDK_796.4_675.4 131 126.1762726 EGLN_GPITSAAELNDPQSILLR_632.4_826.5 132 125.7658253 IL12B_YENYTSSFFIR_713.8_293.1 133 125.0476631 B2MG_VEHSDLSFSK_383.5_234.1 134 124.9154706 PGH1_AEHPTWGDEQLFQTTR_639.3_765.4 135 124.8913193 INHBE_ALVLELAK_428.8_331.2 136 124.0109276 HYOU1_LPATEKPVLLSK_432.6_460.3 137 123.1900369 CXCL2_CQCLQTLQGIHLK_13p48RT_533.6_567.4 138 122.8800873 PZP_AVGYLITGYQR_620.8_523.3 139 122.4733204 AFAM_IAPQLSTEELVSLGEK_857.5_333.2 140 122.4707849 ICAM1_VELAPLPSWQPVGK_760.9_400.3 141 121.5494206 CHL1_VIAVNEVGR_478.8_284.2 142 119.0877137 APOB_ITENDIQIALDDAK_779.9_632.3 143 118.0222045 SAMP_AYSDLSR_406.2_577.3 144 116.409429 AMBP_AFIQLWAFDAVK_704.9_836.4 145 116.1900846 EGLN_GPITSAAELNDPQSILLR_632.4_601.4 146 115.8438804 LRP1_NAVVQGLEQPHGLVVHPLR_688.4_890.6 147 114.539707 SHBG_VVLSSGSGPGLDLPLVLGLPLQLK_791.5_598.4 148 113.1931134 IBP1_AQETSGEEISK_589.8_979.5 149 112.9902709 PSG6_SNPVTLNVLYGPDLPR_585.7_817.4 150 112.7910917 APOC3_DYWSTVK_449.7_347.2 151 112.544736 C1R_WILTAAHTLYPK_471.9_621.4 152 112.2199708 ANGT_ADSQAQLLLSTVVGVFTAPGLHLK_822.5_983.6 153 111.9634671 PSG9_DVLLLVHNLPQNLPGYFWYK_810.4_328.2 154 111.5743214 A1AT_AVLTIDEK_444.8_605.3 155 111.216651 PSGx_ILILPSVTR_506.3_785.5 156 110.8482935 THRB_ELLESYIDGR_597.8_710.4 157 110.7496103 SHBG_ALALPPLGLAPLLNLWAKPQGR_770.5_256.2 158 110.5091269 PZP_QTLSWTVTPK_580.8_545.3 159 110.4675104 SHBG_ALALPPLGLAPLLNLWAKPQGR_770.5_457.3 160 110.089808 PSG4_TLFIFGVTK_513.3_811.5 161 109.9039967 PLMN_YEFLNGR_449.7_293.1 162 109.6880397 PEPD_AVYEAVLR_460.8_587.4 163 109.3697285 PLMN_LSSPAVITDK_515.8_830.5 164 108.963353 FINC_SYTITGLQPGTDYK_772.4_352.2 165 108.452612 C1R_WILT_AAHTL_YPK_471.9_407.2 166 107.8348417 CHL1_TAVTANLDIR_537.3_288.2 167 107.7278897 TENA_AVDIPGLEAATPYR_736.9_286.1 168 107.6166195 CRP_YEVQGEVFTKPQLWP_911.0_293.1 169 106.9739589 APOB_SVSLPSLDPASAK_636.4_885.5 170 106.5901668 PRDX2_SVDEALR_395.2_488.3 171 106.2325046 CO8A_YHFEALADTGISSEFYDNANDLLSK_940.8_301.1 172 105.8963287 C1QC_FQSVFTVTR_542.8_722.4 173 105.4338742 PSGx_ILILPSVTR_506.3_559.3 174 105.1942655 VCAM1_TQIDSPLSGK_523.3_703.4 175 105.0091767 VCAM1_NTVISVNPSTK_580.3_845.5 176 104.8754444 CSH_ISLLLIESWLEPVR_834.5_500.3 177 104.6158295 HGFA_EALVPLVADHK_397.9_439.8 178 104.3383142 CGB1_CRPINATLAVEK_457.9_660.4 179 104.3378072 APOB_IEGNLIFDPNNYLPK_874.0_414.2 180 103.9849346 C1QB_LEQGENVFLQATDK_796.4_822.4 181 103.9153207 APOH_EHSSLAFWK_552.8_838.4 182 103.9052103 CO5_LQGTLPVEAR_542.3_842.5 183 103.1061869 SHBG_1ALGGLLFPASNLR_481.3_412.3 184 102.2490294 B2MG_VNHVTLSQPK_374.9_459.3 185 102.1204362 APOA2_SPELQAEAK_486.8_659.4 186 101.9166647 FLNA_TGVAVNKPAEFTVDAK_549.6_258.1 187 101.5207852 PLMN_YEFLNGR_449.7_606.3 188 101.2531011

TABLE-US-00064 TABLE 63 Random Forest Importance Values Using Peak Area Ratios Variable Rank Importance HABP2_FLNWIK_ 1 3501.905733 410.7_561.3 ADA12_FGFGGST 2 3136.589992 DSGPIR_ 649.3_946.5 A1BG_ 3 2387.891934 LLELTGPK_ 435.8_227.2 B2MG_ 4 1431.31771 VEHSDLSFSK_ 383.5_234.1 ADA12_FGFGGST 5 1400.917331 DSGPIR_ 649.3_745.4 B2MG_ 6 1374.453629 VEHSDLSFSK_ 383.5_468.2 APOB_ 7 1357.812445 IEGNLIFDPNN YLPK_ 874.0_414.2 PSG9_DVLLLVHNL 8 1291.934596 PQNLPGYFWYK_ 810.4_960.5 A1BG_ 9 1138.712941 LLELTGPK_ 435.8_644.4 ITIH3_ALDLSLK_ 10 1137.127027 380.2_185.1 ENPP2_TYLHTYESEI_ 11 1041.036693 628.3_908.4 IL12B_ 12 970.1662913 YENYTSSFFIR_ 713.8_293.1 ENPP2_WWGGQPL 13 953.0631062 WITATK_ 772.4_373.2 ENPP2_TYLHTYESEI_ 14 927.3512901 628.3_515.3 PSG9_LFIPQITR_ 15 813.9965357 494.3_614.4 MAGE 16 742.2425022 ENPP2_WWGGQPL 17 731.5206413 WITATK_ 772.4_929.5 CERU_ 18 724.7745695 TTIEKPVWLGFL GPIIK_ 638.0_640.4 ITIH3_ALDLSLK_ 19 710.1982467 380.2_575.3 PSG2_IHPSYTNYR_ 20 697.4750893 384.2_452.2 ITIH1_LWAYLTI 21 644.7416886 QELLAK_ 781.5_371.2 INHBE_ 22 643.008853 ALVLELAK_ 428.8_331.2 HGFA_ 23 630.8698445 LHKPGVYTR_ 357.5_692.4 TRFL_ 24 609.5866675 YYGYTGAFR_ 549.3_450.3 THBG_ 25 573.9320948 AVLHIGEK_ 289.5_348.7 GELS_ 26 564.3288862 TASDFITK_ 441.7_710.4 PSG9_LFIPQITR_ 27 564.1749327 494.3_727.4 VGFR3_SGVDLA 28 563.8087791 DSNQK_ 567.3_662.3 INHA_ 29 554.210214 TTSDGGYSFK_ 531.7_860.4 PSG9_DVLLLVHNL 30 545.1743627 PQNLPGYFWYK_ 810.4_328.2 HYOU1_LPATEK 31 541.6208032 PVLLSK_ 432.6_347.2 C08G_ 32 541.3193428 VQEAHLTEDQIFYFPK_ 655.7_701.4 BMI 33 540.5028818 HGFA_ 34 536.6051948 LHKPGVYTR_ 357.5_479.3 PSG2_IHPSYTNYR_ 35 536.5363489 384.2_338.2 GELS_ 36 536.524931 AQPVQVAEGSEPDGFW EALGGK_ 758.0_623.4 PSG6_SNPVTLNVLYG 37 520.108646 PDLPR_ 585.7_654.4 HABP2_FLNWIK_ 38 509.0707814 410.7_560.3 PGH1_ILPSVPK_ 39 503.593718 377.2_527.3 HYOU1_LPATEKPVL 40 484.047422 LSK_ 432.6_460.3 C06_ALNHLPLEYNSA 41 477.8773179 LYSR_ 621.0_696.4 INHBE_ 42 459.1998276 ALVLELAK_ 428.8_672.4 PLMN_ 43 452.9466414 LSSPAVITDK_ 515.8_743.4 PSG9_DVLLLVHNLPQ 44 431.8528248 NLPGYFWYK_ 810.4_215.1 BGH3_LTLLAPLNSVFK_ 45 424.2540315 658.4_875.5 AFAM_ 46 421.4953221 LPNNVLQEK_ 527.8_730.4 ITIH2_LSNENHGIAQR_ 47 413.1231437 413.5_519.8 GELS_ 48 404.2679723 TASDFITK_ 441.7_781.4 FETUA_ 49 400.4711207 AHYDLR_ 387.7_566.3 CERU_ 50 396.2873451 TTIEKPVWLGFLGPI1K_ 638.0_844.5 PSGx_ILILPSVTR_ 51 374.5672526 506.3_785.5 APOB_ 52 371.1416438 SVSLPSLDPASAK_ 636.4_885.5 FLNA_ 53 370.4175588 TGVAVNKPAEFTVDAK_ 549.6_258.1 PLMN_ 54 367.2768078 YEFLNGR_ 449.7_606.3 PSGx_ILILPSVTR_ 55 365.7704321 506.3_559.3

[0191] From the foregoing description, it will be apparent that variations and modifications can be made to the invention described herein to adopt it to various usages and conditions. Such embodiments are also within the scope of the following claims.

[0192] The recitation of a listing of elements in any definition of a variable herein includes definitions of that variable as any single element or combination (or subcombination) of listed elements. The recitation of an embodiment herein includes that embodiment as any single embodiment or in combination with any other embodiments or portions thereof.

[0193] All patents and publications mentioned in this specification are herein incorporated by reference to the same extent as if each independent patent and publication was specifically and individually indicated to be incorporated by reference.