Systematic prediction of drug resistance caused by transporter genes in cancer cells.

ABSTRACT: To study the drug resistance problem caused by transporters, we leveraged multiple large-scale public data sets of drug sensitivity, cell line genetic and transcriptional profiles, and gene silencing experiments. Through systematic integration of these data sets, we built various machine learning models to predict the difference between cell viability upon drug treatment and the silencing of its target across the same cell lines. More than 50% of the models built with the same data set or with independent data sets successfully predicted the testing set with significant correlation to the ground truth data. Features selected by our models were also significantly enriched in known drug transporters annotated in DrugBank for more than 60% of the models. Novel drug-transporter interactions we

SUBMITTER: Shen Y 

PROVIDER: S-EPMC8016963 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature