Tissue-specific identification of multi-omics features for pan-cancer drug response prediction.

ABSTRACT: Current statistical models for drug response prediction and biomarker identification fall short in leveraging the shared and unique information from various cancer tissues and multi-omics profiles. We developed mix-lasso model that introduces an additional sample group penalty term to capture tissue-specific effects of features on pan-cancer response prediction. The mix-lasso model takes into account both the similarity between drug responses (i.e., multi-task learning), and the heterogeneity between multi-omics data (multi-modal learning). When applied to large-scale pharmacogenomics dataset from Cancer Therapeutics Response Portal, mix-lasso enabled accurate drug response predictions and identification of tissue-specific predictive features in the presence of various degrees of missing d

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC9385562 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature