Abstract: A cumulant-based network analysis visualizer (CuNAviz) includes an interactive dashboard with a user interface and a display that allows a user to query a network of multi-modal biomarkers for phenotypes associated with a complex disease and to visualize answers to the queries as subgraphs. The subgraphs include highlighted nodes and edges where the highlighted nodes represent the multi-modal biomarkers from the network that are associated with the queried phenotypes for the complex disease and the highlighted edges represent the interactions between the multi-modal biomarkers that are associated with the queried phenotypes for the complex disease. The CuNAviz allows a user to identify important multi-modal biomarkers and neighborhoods of multi-modal biomarkers specific to a complex disease.

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: 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 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 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 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 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: 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.