Research Area:  Machine Learning

Abstract:

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 response in tumors.

Keywords:  
Deep neural networks
Pharmacogenomics
Drug response prediction
Cancer cell line encyclopedia
Genomics of Drug Sensitivity in Cancer
The Cancer Genome Atlas

Author(s) Name:   Yu-Chiao Chiu, Hung-I Harry Chen, Tinghe Zhang, Songyao Zhang, Aparna Gorthi, Li-Ju Wang, Yufei Huang

Publisher name:  Springer

DOI:  10.1186/s12920-018-0460-9

Volume Information:  volume 12

Paper Link:   https://bmcmedgenomics.biomedcentral.com/articles/10.1186/s12920-018-0460-9