A process of determining whether a patient with a disease or disorder will be responsive to a drug, used to treat the disease or disorder, including obtaining a test spectrum produced by a mass spectrometer from a serum produced from the patient. The test spectrum may be processed to determine a relation to a group of class labeled spectra produced from respective serum from other patients having the or similar clinical stage same disease or disorder and known to have responded or not responded to the drug. Based on the relation of the test spectrum to the group of class labeled spectra, a determination may be made as to whether the patient will be responsive to the drug.

Embodiments of the present invention may provide the capability to predict the metastasis of cancer in a patient from one tissue to another. In an embodiment, a computer-implemented method for predicting metastasis may comprise receiving an indication of at least one disrupted gene of the cancer, traversing data representing a gene-to-gene or protein-to-protein interaction network specific for a type of the cancer type from a position of the received gene in the network to a position of at least one gene involved in metastasis for a tissue type, organ or body part, determining at least one shortest path in the network between the received gene and the at least one gene involved in metastasis for the tissue type, organ or body part, generating a prediction of metastasis to the tissue type based on the at least one determined path, and generating an output display indicating a likelihood of spread of cancer to the tissue type, organ or body part.

Computational methods for classifying and predicting protein side chain conformations utilizing a data driven scoring function are disclosed. According to some embodiments, the methods may include obtaining structure data representing a plurality of conformations of a compound. The methods may also include determining structural differences among the conformations. The methods may also include classifying, based on the structural differences, the conformations into one or more clusters. The methods may also include determining representative conformations of the dusters, wherein an average structural difference between a representative conformation of a duster and conformations in the duster is below a predetermined threshold. The method may further include determining the representative conformations as poses of the compound.

Processing genetic information comprises: receiving an input that includes information pertaining to a specific genetic variant; and identifying, in a database comprising genotype information of a plurality of candidate individuals, a matching individual imputed to have the specific genetic variant. The genotype information of the matching individual corresponding to the specific genetic variant is not directly assayed.

The present invention relates to compositions and methods for molecular profiling and diagnostics for genetic disorders and cancer, including but not limited to gene expression product markers associated with cancer or genetic disorders. In particular, the present invention provides algorithms and methods of classifying cancer, for example, thyroid cancer, methods of determining molecular profiles, and methods of analyzing results to provide a diagnosis.

The present invention relates to a method and system for associating gene expression data with a disease name. A first data set associated with a plurality of genetic probes for a plurality of biological samples may be received. The first data set may be sorted based on a normalized gene expression values for the plurality of genetic probes. A largest value gap of the normalized gene expression values may be identified. A set of expressed genes within the first data set may be identified. An indexable document may be generated for a biological sample of the plurality of biological samples comprising data associated with the set of expressed genes. A second data set associated with an expressed gene of the set of expressed genes may be searched. A disease name may be associated with an expressed gene based on a threshold correlation between the disease name and the expressed gene.

Estimating parental contribution of ancestry includes: obtaining a set of ancestry assignment data associated with an individual's genotype data, at least some of the ancestry assignment data indicating that one or more segments of the individual's genotype data is deemed to be associated with a specific ancestry; determining whether in the individual's genotype data there is at least one confirmed region of overlapping ancestry assignment associated with the specific ancestry; in the event that it is determined that there is at least one confirmed region of overlapping ancestry assignment associated with the specific ancestry: specifying that parental contribution of the specific ancestry is made by both parents of the individual; in the event that it is determined that there is no confirmed region of overlapping ancestry assignment associated with the specific ancestry: statistically determining whether the parental contribution to the specific ancestry is made by only one parent of the individual or by both parents of the individual, the determination being based at least in part on one or more lengths of the one or more segments deemed to be associated with the specific ancestry; and outputting information pertaining to the parental contribution to the specific ancestry.

Embodiments in the disclosure are directed to the use of distributed computing to align reads against multiple portions of a reference dataset. Aligned portions of the reference dataset that correspond with an above-threshold alignment score can be assessed for the presence of sparse indicators that can be categorized and used to influence a determination of a state transition likelihood. Various tasks associated with the processing of reads (e.g., alignment, sparse indicator detection, and/or determination of a state transition likelihood) may be able to take advantage of parallel processing and can be distributed among the machines while considering the resource utilization of those machines. Different load-balancing mechanisms can be employed in order to achieve even resource utilization across the machines, and in some cases may involve assessing various processing characteristics that reflect a predicted resource expenditure and/or time profile for each task to be processed by a machine.

A method for at least one of characterizing, diagnosing, and treating an autoimmune disorder in at least a subject, the method comprising: receiving an aggregate set of biological samples from a population of subjects; generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects; generating a characterization of the autoimmune condition based upon features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; based upon the characterization, generating a therapy model configured to correct the autoimmune condition; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.

A method and system is provided for representing compositional properties of a biological sequence fragment and application thereof. The present application provides a method and system for representing compositional properties of a biological sequence fragment using a unidimensional compositional metric; comprising of collecting a plurality of biological sequence fragments; sequencing collected plurality of biological sequence fragments; generating a first set of reference vectors; computing a unidimensional compositional metric for each sequenced biological sequence fragment out of the plurality of sequenced biological sequence fragments as a cumulative function of the distance of the tetra-nucleotide frequency vector (v) from three or more reference vectors selected out of the generated first set of reference vectors; and segregating each sequenced biological sequence fragment out of the plurality of sequenced biological sequence fragments in to a plurality of groups based on respective unidimensional compositional metric.