Abstract: A computer-implemented method for data analysis is provided. The computer-implemented method includes splitting data into first and second training data and first and second test data, partitioning the first and second training data into first and second partitions, generating initial Betti curves for the first and second partitions, training a model to recognize Betti curves of the first and second data types on the initial Betti curves, adding each of the first and second test data to each of the first and second partitions to form new first and second partitions, generating new Betti curves for the new first and second partitions, having the model determine whether each of the first and second test data are likely to be first or second data types from new and initial Betti curve deviations and iteratively re-training the model based on determination accuracies.

Abstract: Systems and techniques that facilitate mapping of data graphs to quantum computer backend topologies are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory that can execute the computer executable components stored in memory. The computer executable components can comprise a mapping component that maps a data graph to a lattice structure of the quantum computer, wherein the mapping comprises: mapping a graph node of the data graph to a lattice node of the lattice structure; and mapping the immediate descendant nodes and grand-descendant nodes of the graph node to nodes of the lattice structure based on relative number of edges of the immediate descendant nodes and grand-descendant nodes and the nodes of the lattice structure.

Abstract: A set of candidate drugs is selected based on one or more outcomes related to one or more diseases and variants linked with the selected set of candidate drugs are obtained. One or more protein sequences related to the selected set of candidate drugs are collated. Pairs of variants and protein sequences are generated for each drug of the set of candidate drugs. A contrastive learning model is trained using the generated pairs of variants and protein sequences and a downstream task is performed using the contrastive learning model.

Abstract: A computer-implemented method includes receiving at a processor an input set of multiway data. The method determines a set of persistent homology barcodes based on the multiway data and identifies at least a first significant persistent homology barcode in the set of persistent homology barcodes. A representative cycle of the first significant persistent homology is returned and an orthonormal basis for a boundary of the multiway data is computed. The method obtains a harmonic representative by computing a projection of the representative cycle to an orthogonal complement and generates a set of feature vectors using the harmonic representative. Each feature vector has a magnitude corresponding to an impact that feature has on a given condition. The method then ranks the feature vectors by feature vector magnitude.

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 receiving at a processor an input set of multiway data. The method determines a set of persistent homology barcodes based on the multiway data and identifies at least a first significant persistent homology barcode in the set of persistent homology barcodes. A representative cycle of the first significant persistent homology is returned and an orthonormal basis for a boundary of the multiway data is computed. The method obtains a harmonic representative by computing a projection of the representative cycle to an orthogonal complement and generates a set of feature vectors using the harmonic representative. Each feature vector has a magnitude corresponding to an impact that feature has on a given condition. The method then ranks the feature vectors by feature vector magnitude.

Abstract: A method for capturing distinct higher order interactions of a dataset relevant to biological inferences includes deriving Hamiltonian parameters for the dataset, wherein the dataset includes data responsive to a phenotype of interest. The method further includes generating a partition function responsive to the Hamiltonian parameters; calculating cumulant moments of the partition function and deriving higher order cumulants using the Hamiltonian parameters, wherein the higher order cumulants are responsive to the phenotype of interest.

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 for data analysis is provided. The computer-implemented method includes splitting data into first and second training data and first and second test data, partitioning the first and second training data into first and second partitions, generating initial Betti curves for the first and second partitions, training a model to recognize Betti curves of the first and second data types on the initial Betti curves, adding each of the first and second test data to each of the first and second partitions to form new first and second partitions, generating new Betti curves for the new first and second partitions, having the model determine whether each of the first and second test data are likely to be first or second data types from new and initial Betti curve deviations and iteratively re-training the model based on determination accuracies.

Abstract: A computer-implemented method for determining a predictive setup for an experiment includes receiving at a processor an input set of multiway data. The multiway data includes a numerical representation of each factor in a set of interconnected factors affecting an outcome. Each factor has a codependency on at least one other factor in the set of interconnected factors. The method determines a set of persistent homology barcodes based on the multiway data using the processer and identifies at least a first significant persistent homology barcode in the set of persistent homology barcodes. A representative cycle of the first significant persistent homology is returned and an orthonormal basis of the multiway data is computed. A harmonic representative is obtained by computing a projection of the representative cycle to an orthogonal complement.

Abstract: A computer-implemented method includes receiving an input set of multiway data. The multiway data includes a numerical representation of each factor in a set of interconnected factors affecting an outcome and each factor has a codependency on at least one other factor in the set of interconnected factors. A set of persistent homology barcodes is determined based on the multiway data using a processer. At least a first significant persistent homology barcode in the determined set of persistent homology barcodes is identified and a representative cycle of the first significant persistent homology is returned. An orthonormal basis of the multiway data is computed. A harmonic representative is obtained by computing a projection of the representative cycle to an orthogonal complement, and a treatment plan is determined based on the harmonic representation and the treatment plan is output to a user.

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: An AI platform is used for developing a combination therapy for a patient afflicted with a tumor that has produced clones. The combination therapy, which includes at least two perturbations, is capable of targeting clones (including subclones) that have escaped therapeutic intervention due to resistance and/or evolution. The AI platform is trained with perturbation data obtained from at least one cell line that has similar characteristics to a clone of interest. The trained AI platform predicts how the clone of interest will respond to perturbations and ranks the perturbation responses from highest to lowest. The at least one cell line may be an existing cell line from a well-established database or a synthetic cell line generated by the AI platform. The AI platform may include one or more of a machine learning platform, a deep learning platform, an artificial neural network (ANN), a convolution neural network (CNN), and a generative adversarial network (GAN).

Abstract: Topological data analysis is used to detect general nestedness in microbial communities through the generation and application of filtration matrices and persistent homology barcodes. From a starting point of an input matrix with 1s and 0s, a filtration matrix is generated with a mathematical function, such as a Jaccard similarity, overlap coefficient, or a min(com1/sum(1)) computation. The persistent homology of the filtration matrix is then calculated in at least two dimensions using a mathematical function, such as a simplicial complex, a cubical complex, an alpha complex, a C?ech complex, or a Vietris-Rips complex, that is displayed in a barcode that provides a visualization of the nestedness within the microbial community. P-values for the microbial community nestedness can be calculated by comparing the shape and length of the persistent homology barcodes for the input matrix against persistent homology barcodes for a completely randomized input matrix.

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 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: 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.