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: A method, computer system, and a computer program product for biomarker identification is provided. The present invention may include generating a plurality of higher-order joint cumulants based on an input data matrix. The present invention may include identifying one or more significant higher-order joint cumulant groups from the plurality of higher-order joint cumulants. The present invention may include embedding the one or more significant higher-order joint cumulant groups into a lower dimensional network. The present invention may include identifying one or more biomarkers.

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 system, method, and computer program product that includes a computer readable storage medium with program instructions, executable by a processer, to cause a device to perform the method. The method includes receiving a set of biomarkers associated with a known phenotype, generating at least one ranking for each biomarker based on a feature selection method, selecting a set of potential key biomarkers from the set of biomarkers based on the ranking, and selecting a set of key biomarkers from the potential key biomarkers. The method also includes building a model for phenotype prediction based on the set of key biomarkers.

Abstract: A computer-implemented method includes generating, by a processor, a set of training data for each phenotype in a database including a set of subjects. The set of training data is generated by dividing genomic information of N subjects selected with or without repetition into windows, computing a distribution of genomic events in the windows for each of N subjects, and extracting, for each window, a tensor that represents the distribution of genomic events for each of N subjects. A set of test data is generated for each phenotype in the database, a distribution of genomic events in windows for each phenotype is computed, and a tensor is extracted for each window that represents a distribution of genomic events for each phenotype. The method includes classifying each phenotype of the test data with a classifier, and assigning a phenotype to a patient.

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 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, computer program product, and computer processing system are provided for metagenomic pattern classification. The method includes pre-processing, by a processor, a taxonomy tree associated with a genome database to extract taxonomy related information therefrom. The genome database includes a plurality of genome sequences. The method further includes building, by the processor, a suffix tree on the genome database. The method also includes annotating, by the processor, nodes in the suffix tree, using a plurality of right maximal patterns derived from the extracted taxonomy related information as annotations, such that each of the plurality of right maximal patterns in the suffix tree points to a respective one of a plurality of nodes in the taxonomy tree and such that a leaf node in the taxonomy tree represents a respective sample organism. The annotations are configured to function as classifications for the plurality of genome sequences.

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 method of electronically separating host and non-host sequence data (sequenced DNA, RNA, and/or proteins) utilizes electronic host filters, which can be generated on a just-in-time basis by a cloud-based software service. Host reads and non-host reads of a given sample are separated and stored in separate data repositories. Also disclosed is a cloud-based software service utilizing the method. The non-host reads resulting from the host filtration process can then be profiled more accurately for the microorganism content therein.

Abstract: Embodiments of the present invention are directed to methods for adapting machine learning, redescription, and computational homology techniques to the identification of pathogenic pathways. A non-limiting example of the computer-implemented method includes receiving genetic and biological data and generating a data matrix based on the data. The data matrix can include one or more features, and each feature can be associated with a known feature value. A collection of sets of features representing pathways, genes, or a genetic combination of genotype values can be determined. The method also includes determining a first prediction for a feature value of a selected feature to be predicted in the collection, permuting one or more rows of the data matrix, and recalculating a second prediction for the feature value based on the permutation. A prediction score can be determined based on the first prediction, the second prediction, and a known feature value.

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 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: A computer-implemented method includes generating, by a processor, a set of training data for each phenotype in a database including a set of subjects. The set of training data is generated by dividing genomic information of N subjects selected with or without repetition into windows, computing a distribution of genomic events in the windows for each of N subjects, and extracting, for each window, a tensor that represents the distribution of genomic events for each of N subjects. A set of test data is generated for each phenotype in the database, a distribution of genomic events in windows for each phenotype is computed, and a tensor is extracted for each window that represents a distribution of genomic events for each phenotype. The method includes classifying each phenotype of the test data with a classifier, and assigning a phenotype to a patient.