Abstract: A computational method, apparatus and system for diagnostic and therapeutic prediction from multimodal data is provided for using machine learning to predict medical therapeutic methods using multimodal data. The computational method, apparatus and system for diagnostic and therapeutic prediction from multimodal data may include a biomarker and subtype identification aspect, multimodality aspect, machine learning aspect, and training aspect. A method for using machine learning to predict medical therapeutic methods, which may include targets, drugs, or combinations of drugs, using multimodal data using the computational system for diagnostic and therapeutic prediction from multimodal data is also provided.

Abstract: The present disclosure provides systems and methods of classifying and/or identifying a cancer subtype. The present disclosure also provides methods of enhancing the prediction of a tumor mutational burden by using both synonymous and non-synonymous somatic mutations in the computation method. It is believed that by increasing the number of mutations in the computation of the tumor mutational burden, a comparatively more consistent tumor mutational burden may be derived, especially for targeted-panel sequencing. It is believed that the consistent computation of the tumor mutational burden from targeted panels allows for computationally quicker and less costly analysis of sequencing data as compared with a tumor mutational burden computed from whole exome sequencing data.

Abstract: A hybrid computational system of classical and quantum computing for drug discovery is provided for discovering drugs showing efficacy in affecting the behavior of a biological subject. The hybrid computational system of classical and quantum computing for drug discovery may include a computing environment, classical computing aspect, quantum computing aspect, compute workflow and machine learning operation. A method for discovering drugs showing efficacy in affecting the behavior of a biological subject using the hybrid computational system of classical and quantum computing for drug discovery is also provided.

Abstract: The present disclosure provides systems and methods that utilize neural networks such as convolutional neural networks to analyze genomic sequence data generated by a sequencer and generate accurate prediction data identifying and describing germline and/or somatic variants within the sequence data.

Abstract: Described herein are methods, systems, and apparatuses for detecting significantly mutated genes/pathways in a cancer cohort. A driver gene detection technique taking into account the heterogeneous mutational context in a cancer cohort is disclosed. A statistical model of a gene-specific mutation rate distribution (e.g., using an optimized gene specific mean estimation and/or a gene-specific dispersion estimation) is used to model a sample/gene-specific background mutation rate. The statistical model may then be used to detect gene/pathway enrichment and distinguish tumor suppressors and oncogenes based on the spatial distribution of non-silent mutations, loss-of-function mutations, and/or gain-of-function mutations.