Machine learning based anti-cancer drug response prediction and search for predictor genes using cancer cell line gene expression.

ABSTRACT: Although many models have been proposed to accurately predict the response of drugs in cell lines recent years, understanding the genome related to drug response is also the key for completing oncology precision medicine. In this paper, based on the cancer cell line gene expression and the drug response data, we established a reliable and accurate drug response prediction model and found predictor genes for some drugs of interest. To this end, we first performed pre-selection of genes based on the Pearson correlation coefficient and then used ElasticNet regression model for drug response prediction and fine gene selection. To find more reliable set of predictor genes, we performed regression twice for each drug, one with IC50 and the other with area under the curve (AUC) (or activity area)

SUBMITTER: Qiu K 

PROVIDER: S-EPMC8042299 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature