Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines.

ABSTRACT: Computational models for drug sensitivity prediction have the potential to significantly improve personalized cancer medicine. Drug sensitivity assays, combined with profiling of cancer cell lines and drugs become increasingly available for training such models. Multiple methods were proposed for predicting drug sensitivity from cancer cell line features, some in a multi-task fashion. So far, no such model leveraged drug inhibition profiles. Importantly, multi-task models require a tailored approach to model interpretability. In this work, we develop DEERS, a neural network recommender system for kinase inhibitor sensitivity prediction. The model utilizes molecular features of the cancer cell lines and kinase inhibition profiles of the drugs. DEERS incorporates two autoencoders to project

SUBMITTER: Koras K 

PROVIDER: S-EPMC8346627 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature