Abstract: Provided are methods, systems and computer program product embodiments for analyzing multi-omic data using a tensor regression model for genome-wide association studies in the life sciences. The unique structure of tensor covariates is leveraged to find associations between the omics data and complex diseases. Within this framework, the excessive dimensionality is reduced to a manageable level, leading to efficient estimations and predictions. The method is superior to using classical regression techniques in genome-wide association studies, which are challenged by analyzing multi-dimensional and uniquely structured data from the health and life sciences, in which covariates can take on more intricate forms such as multi-dimensional arrays.

Abstract: A plurality of data pairs are created by matching an element from a first modality with an element from a second modality. Each element from the first modality and each element from the second modality are tokenized to obtain first modality tokens and second modality tokens. A model is trained based on the plurality of data pairs, the training comprising learning a first embedding from the first modality tokens via a first attention-based encoder for the first modality and a second embedding from the second modality tokens via a second attention-based encoder for the second modality, calculating a cosine similarity between the first embedding and the second embedding for each data pair and computing a loss between predicted items and ground truth based on the cosine similarity. The predicted items with a minimal loss are validated to obtain at least one candidate therapeutic.

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 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.