The need for an accurate, early, and precise estimation of expected delivery date (EDD) for the pregnant subject is vital. A system and method for predicting a day/date of delivery for a pregnant subject using one or more microbiome samples collected from the pregnant subject is provided. The disclosure relates to applying machine learning techniques on the microbiome characterization data corresponding to the biological sample(s) collected from the pregnant subject. The method further comprises using the predicted EDD to suitably plan and take required medical treatment or precautions or medical advice for the pregnant subject to prevent any pregnancy and/or delivery related complications and to manage the delivery appropriately. The disclosure also provides compositions of the microbiome data which can potentially influence the delivery date, or the method provides exemplary compositions of the microbiome data which plays vital role in estimating the EDD of the pregnant subject.

The need for an accurate, early, and precise estimation of expected delivery date (EDD) for the pregnant subject is vital. A system and method for predicting a day/date of delivery for a pregnant subject using one or more microbiome samples collected from the pregnant subject is provided. The disclosure relates to applying machine learning techniques on the microbiome characterization data corresponding to the biological sample(s) collected from the pregnant subject. The method further comprises using the predicted EDD to suitably plan and take required medical treatment or precautions or medical advice for the pregnant subject to prevent any pregnancy and/or delivery related complications and to manage the delivery appropriately. The disclosure also provides compositions of the microbiome data which can potentially influence the delivery date, or the method provides exemplary compositions of the microbiome data which plays vital role in estimating the EDD of the pregnant subject.

The technology disclosed relates to a variant pathogenicity classifier. The variant pathogenicity classifier comprises memory and runtime logic. The memory stores (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein. The runtime logic has access to the memory, and is configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.

The technology disclosed relates to a variant pathogenicity classifier. The variant pathogenicity classifier comprises memory and runtime logic. The memory stores (i) a reference amino acid sequence of a protein, (ii) an alternative amino acid sequence of the protein that contains a variant amino acid caused by a variant nucleotide, and (iii) a protein contact map of the protein. The runtime logic has access to the memory, and is configured to provide (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map as input to a first neural network, and to cause the first neural network to generate a pathogenicity indication of the variant amino acid as output in response to processing (i) the reference amino acid sequence, (ii) the alternative amino acid sequence, and (iii) the protein contact map.

The lack of clear predictors of prostate cancer progression leads to subjective decision-making regarding courses of treatment. The identification of new biomarkers that are predictive of recurrence after radical prostatectomy would advance the field of prostate cancer treatment. Disclosed are miRNAs that can be used as molecular biomarkers to detect or predict the progression of prostate cancer and to adjust a treatment plan accordingly. Furthermore, kits are included for the detection of these miRNAs.

Gene expression data provides a basis for more accurate identification and diagnosis of lymphoproliferative disorders. In addition, gene expression data can be used to develop more accurate predictors of survival. The present invention discloses methods for identifying, diagnosing, and predicting survival in a lymphoma or lymphoproliferative disorder on the basis of gene expression patterns. The invention discloses a novel microarray, the Lymph Dx microarray, for obtaining gene expression data from a lymphoma sample. The invention also discloses a variety of methods for utilizing lymphoma gene expression data to determine the identity of a particular lymphoma and to predict survival in a subject diagnosed with a particular lymphoma. This information will be useful in developing the therapeutic approach to be used with a particular subject.

The present invention relates to methods for evaluating the probability that a patient's diagnosis may be treated with a particular clinical regimen or therapy.

The present invention provides a method of assessing whether an individual is at high risk or low risk of inflammatory bowel disease (IBD) progression by determining the expression level of two or more genes in a whole blood sample. Also provided are methods for treating IBD in an individual who is determined to be at high risk or low risk for IBD progression, and kits for assessing whether an individual is at high risk or low risk for IBD progression. Arrays, and methods of providing arrays, of patient-identified selected gene expression products from a whole blood sample of a patient are also provided.

Differential expression of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) are used to diagnose diseases including neurological diseases, inflammatory diseases, rheumatic diseases, and autoimmune diseases. Machine learning systems are used to identify lncRNAs or eRNAs having differential expression correlated with certain disease states.

Differential expression of long non-coding RNAs (lncRNAs) and enhancer RNAs (eRNAs) are used to diagnose diseases including neurological diseases, inflammatory diseases, rheumatic diseases, and autoimmune diseases. Machine learning systems are used to identify lncRNAs or eRNAs having differential expression correlated with certain disease states.