Predicting drug response of tumors from integrated genomic profiles by deep neural networks.

ABSTRACT:

Background

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug respo

SUBMITTER: Chiu YC 

PROVIDER: S-EPMC6357352 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature