Hierarchical multi-omics data integration and modeling predict cell-specific chemical proteomics and drug responses.

ABSTRACT: Drug-induced phenotypes result from biomolecular interactions across various levels of a biological system. Characterization of pharmacological actions therefore requires integration of multi-omics data. Proteomics profiles, which may more directly reflect disease mechanisms and biomarkers than transcriptomics, have not been widely exploited due to data scarcity and frequent missing values. A computational method for inferring drug-induced proteome patterns would therefore enable progress in systems pharmacology. To predict the proteome profiles and corresponding phenotypes of an uncharacterized cell or tissue type that has been disturbed by an uncharacterized chemical, we developed an end-to-end deep learning framework: TransPro. TransPro hierarchically integrated multi-omics data, in lin

SUBMITTER: Wu Y 

PROVIDER: S-EPMC10163019 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature