Leveraging TCGA gene expression data to build predictive models for cancer drug response.
Journal:
BMC bioinformatics
Published Date:
Sep 30, 2020
Abstract
BACKGROUND: Machine learning has been utilized to predict cancer drug response from multi-omics data generated from sensitivities of cancer cell lines to different therapeutic compounds. Here, we build machine learning models using gene expression data from patients' primary tumor tissues to predict whether a patient will respond positively or negatively to two chemotherapeutics: 5-Fluorouracil and Gemcitabine.