Abstract: Embodiments are directed to a computer-implemented method that includes using a processor system to encode binary risk factor variables, genotypic risk factor variables, and continuous risk factor variables. The processor system is further used to adversarially train a multivariate Gaussian (MVG) generative model to generate synthetic versions of the binary risk factor variables, synthetic versions of the genotypic risk factor variables, and synthetic versions of the continuous risk factor variables.

Abstract: A method is provided for training a predicting cfDNA shedding model using a plurality of lesion and cfDNA datasets. A new cfDNA shedding sample and the plurality of lesion and cfDNA datasets are clustered to predict a shedding pattern. A diagnostic type is determined for a subsequent cfDNA shedding sample based on the predicted shedding pattern.

Abstract: Methods and systems for training a machine learning model are described. A processor can transform single cell data in a first space into projection data in a second space having a dimensionality lower than or equal to the first space. The processor can produce a cover having a plurality of sets of the projection data. The processor can determine a plurality of transition paths among the plurality of sets. A transition path can represent a transition from one cell state to another cell state. The processor can translate the transition paths from the second dimensional space to the first dimensional space. The processor can extract features from the transition paths in the first dimensional space. The processor can generate training data using the features, and use the training data to train a machine learning model for classifying cell state transitions.

Abstract: A computer-implemented method incudes calculating, by a processor, based on sequence data for a tumor from a subject at a plurality of time points, a mutation frequency for each of a plurality of SSVs at each of the time points to provide a plurality of time-resolved mutation frequencies (between 0 and 1) for each of the plurality of SSVs, the sequence data including a plurality of simple somatic variations (SSVs) at each of the time points; binning, by the processor, the plurality of time-resolved mutation frequencies for each SSV at each of the time points to provide a matrix of SSVs and time points; converting, by the processor, the matrix cells to pseudo-clones; and constructing, by the processor, a time-series tumor evolution tree from the pseudo-clones, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment.

Abstract: A computer-implemented method includes determining, by a processor, from a time-series evolution tree comprising one or more clones at each of the plurality of time points, that the one or more clones are sensitive clones or resistant clones, wherein the time-series evolution tree is based on sequence data for a tumor from a subject at a plurality of time points, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment, and wherein a clone is a collection of gene alterations; based at least in part on determining that the one or more clones that are the sensitive or resistant clones, determining, by the processor, a geneset composition of the one or more clones that are the sensitive or resistant clones; and based at least in part on determining the geneset composition, determining by the processor, a further treatment for the subject.

Abstract: Embodiments of the invention include methods, systems, and computer program products for determining stroke onset. Aspects of the invention include determining a baseline behavioral model for a user and receiving real-time user data from a personal portable device. Aspects of the invention also include analyzing the real-time user data to determine the presence of an abnormal event. Aspects of the invention also include, based at least in part on a determination that the abnormal event is present, conducting a plurality of stroke analyses to generate a plurality of impairment characteristics. Aspects of the invention also include integrating the plurality of impairment characteristics, comparing the integrated plurality of impairment characteristics to the baseline behavioral model and outputting a stroke onset determination.

Abstract: Embodiments include methods, systems, and computer program products for analyzing genomic data. Aspects include receiving genomic data for an organism, sample phenotypes, and a plurality of gene sets. Aspects include, for each of the gene sets, determining a set of genes G corresponding to genes in the gene set and a set of genes G? corresponding to genes outside the gene set for the phenotypes R and R?. Aspects also include determining a set of mutated genes M and a set of non-mutated genes M? for R and R? and a mutation enrichment score. Aspects also include determining a set of differentiated genes D a set of non-differentiated genes D? for R and R?. Aspects also include identifying an enriched gene set GE based at least in part upon the mutation enrichment score and the differentiation enrichment score.

Abstract: A computer-implemented method includes inputting, to a processor, an N×K SSV frequency matrix M and an error tolerance ??0, wherein N is a number of SSVs and K is a number of time points, wherein matrix M comprises a plurality of time-resolved mutation frequencies for each SSV; clustering, by the processor, matrix rows in M that satisfy the ? to provide a plurality of SSV clusters; assigning, by the processor, a mean cluster frequency to each SSV within each SSV cluster; calculating errors for removing low frequency rows, for rounding rows to 1 or 0; assigning a root node for all SSV clusters of frequency 1; and calculating, by the processor, a ?-compliant time-series evolution tree with error ?? comprising the root node and a plurality time-stratified nodes, wherein calculating includes assigning a clonal configuration, optionally re-configuring the clonal configuration, and calculating error for re-configuring.

Abstract: A computer-implemented method is disclosed which includes receiving biological sample information from one or more subjects at a first time period. The method further includes receiving biological sample information from the one or more subjects at a second time period. The method further includes comparing the biological sample information at the second time period with the biological sample information at the first time period. The method further includes generating a precedence graph based on results of the comparison. The method further includes determining one or more actions based on the precedence graph.

Abstract: A computer-implemented method includes to determine a cell, tissue or a lesion representation in cell-free DNA comprises inputting, to a processor, cell-free DNA (cfDNA) genomic profiles from one or more fluid biopsy samples from a patient and one or more genomic profiles from one or more cells, tissues or lesions from the patient; constructing, by the processor, a plurality of synthetic fluid hypotheses (SFs); comparing, by the processor, each of the plurality of SFs to the cfDNA genomic profiles to determine goodness of fit, of each of the plurality of SFs; selecting, by the processor, a subset of the plurality of SFs, wherein each SF of the subset of SFs has a minimum distance in goodness of fit compared to the cfDNA genomic profile; and outputting, by the processor, based on the subset of SFs, a cell, tissue or a lesion representation in the cfDNA of the patient.

Abstract: A computer-implemented method includes inputting, to a processor, genomic data from a plurality of subjects, the genomic data including first sample genomic data prior to a treatment, and second sample genomic data after the treatment; determining, by the processor, a plurality of ?'s for the plurality of subjects, wherein each ? is a genetic change in the second sample compared to the first sample genomic data; creating, by the processor, a matrix of the plurality of subjects and their features which features are the genetic changes or clusters of genetic changes in the plurality of ?'s of the subjects; biclustering, by the processor, the matrix of the plurality of subjects and their features, to provide clumps of subjects sharing a common feature such as a shared genetic change or shared cluster of genetic changes; and outputting, by the processor, the clumps of subjects, the common features, and the treatment.

Abstract: A computer-implemented method includes inputting, to a processor, an N×K SSV frequency matrix M and an error tolerance ??0, wherein N is a number of SSVs and K is a number of time points, wherein matrix M comprises a plurality of time-resolved mutation frequencies for each SSV; clustering, by the processor, matrix rows in M that satisfy the ? to provide a plurality of SSV clusters; assigning, by the processor, a mean cluster frequency to each SSV within each SSV cluster; calculating errors for removing low frequency rows, for rounding rows to 1 or 0; assigning a root node for all SSV clusters of frequency 1; and calculating, by the processor, a ?-compliant time-series evolution tree with error ?? comprising the root node and a plurality time-stratified nodes, wherein calculating includes assigning a clonal configuration, optionally re-configuring the clonal configuration, and calculating error for re-configuring.

Abstract: Embodiments are directed to computer implemented method of assessing a relevancy of a pathway to a disease of interest, the pathway having a source and a target. The method includes developing an impact of the source on the pathway. The method further includes developing a value of targeting, based at least in part on an alteration of the pathway, the pathway with a drug of interest. The method further includes identifying a relationship between the source and the target within the pathway. The method further includes combining: the impact of the source on the pathway; the value of targeting, based at least in part on the alteration of the pathway, the pathway with a drug of interest; and the relationship between the source and the target within the pathway, wherein the combining results in an assessment that represents the relevancy of the pathway to the disease of interest.

Abstract: Embodiments are directed to computer implemented method of assessing a relevancy of a pathway to a disease of interest, the pathway having a source and a target. The method includes developing an impact of the source on the pathway. The method further includes developing a value of targeting, based at least in part on an alteration of the pathway, the pathway with a drug of interest. The method further includes identifying a relationship between the source and the target within the pathway. The method further includes combining: the impact of the source on the pathway; the value of targeting, based at least in part on the alteration of the pathway, the pathway with a drug of interest; and the relationship between the source and the target within the pathway, wherein the combining results in an assessment that represents the relevancy of the pathway to the disease of interest.

Abstract: A computer-implemented method includes determining, by a processor, from a time-series evolution tree comprising one or more clones at each of the plurality of time points, that the one or more clones are sensitive clones or resistant clones, wherein the time-series evolution tree is based on sequence data for a tumor from a subject at a plurality of time points, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment, and wherein a clone is a collection of gene alterations; based at least in part on determining that the one or more clones that are the sensitive or resistant clones, determining, by the processor, a geneset composition of the one or more clones that are the sensitive or resistant clones; and based at least in part on determining the geneset composition, determining by the processor, a further treatment for the subject.

Abstract: A computer-implemented method incudes calculating, by a processor, based on sequence data for a tumor from a subject at a plurality of time points, a mutation frequency for each of a plurality of SSVs at each of the time points to provide a plurality of time-resolved mutation frequencies (between 0 and 1) for each of the plurality of SSVs, the sequence data including a plurality of simple somatic variations (SSVs) at each of the time points; binning, by the processor, the plurality of time-resolved mutation frequencies for each SSV at each of the time points to provide a matrix of SSVs and time points; converting, by the processor, the matrix cells to pseudo-clones; and constructing, by the processor, a time-series tumor evolution tree from the pseudo-clones, wherein each time point in the time-series evolution tree represents an event in the subject's cancer treatment.

Abstract: Embodiments include methods, systems, and computer program products for analyzing mutational evolution. Aspects include receiving a whole genome data set for a patient including a plurality of mutations. Aspects also include determining a variant allele frequency for each of the plurality of mutations. Aspects also include labeling each of the plurality of mutations with a gene set designation. Aspects also include constructing an evolution topology comprising an ordered representation of the plurality of mutations, wherein each of the plurality of mutations comprises one of the gene set designations.

Abstract: Methods and systems for genetic diagnosis include splitting genomes into respective groups of non-overlapping windows. The genomes are sampled into sets, each set being made up of selected genomes. A distribution of events is generated across the sets in each window. A tensor is determined for each window based on statistical properties of the distribution of events for the window. A classifier is generated based on the tensors. One or more phenotypes is diagnosed from an input genome using the classifier.

Abstract: Embodiments include methods, systems, and computer program products for analyzing genomic data. Aspects include receiving genomic data for an organism, sample phenotypes, and a plurality of gene sets. Aspects include, for each of the gene sets, determining a set of genes G corresponding to genes in the gene set and a set of genes G? corresponding to genes outside the gene set for the phenotypes R and R?. Aspects also include determining a set of mutated genes M and a set of non-mutated genes M? for R and R? and a mutation enrichment score. Aspects also include determining a set of differentiated genes D a set of non-differentiated genes D? for R and R?. Aspects also include identifying an enriched gene set GE based at least in part upon the mutation enrichment score and the differentiation enrichment score.

Abstract: Embodiments of the invention include methods, systems, and computer program products for determining stroke onset. Aspects of the invention include determining a baseline behavioral model for a user and receiving real-time user data from a personal portable device. Aspects of the invention also include analyzing the real-time user data to determine the presence of an abnormal event. Aspects of the invention also include, based at least in part on a determination that the abnormal event is present, conducting a plurality of stroke analyses to generate a plurality of impairment characteristics. Aspects of the invention also include integrating the plurality of impairment characteristics, comparing the integrated plurality of impairment characteristics to the baseline behavioral model and outputting a stroke onset determination.