期刊
BMC BIOINFORMATICS
卷 19, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12859-018-2509-3
关键词
Machine learning; Deep learning; Combination therapy; in silico drug screening
类别
资金
- Joint Design of Advanced Computing Solutions for Cancer (JDACS4C) program
- National Cancer Institute (NCI) of the National Institutes of Health
- U.S. Department of Energy [DE-AC02-06-CH11357, DE-AC52-07NA27344, DE-AC5206NA25396]
- federal funds from the National Cancer Institute, NIH [HHSN261200800001E]
- Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research
- American Recovery and Reinvestment Act funds
- JDACS4C program
BackgroundThe National Cancer Institute drug pair screening effort against 60 well-characterized human tumor cell lines (NCI-60) presents an unprecedented resource for modeling combinational drug activity.ResultsWe present a computational model for predicting cell line response to a subset of drug pairs in the NCI-ALMANAC database. Based on residual neural networks for encoding features as well as predicting tumor growth, our model explains 94% of the response variance. While our best result is achieved with a combination of molecular feature types (gene expression, microRNA and proteome), we show that most of the predictive power comes from drug descriptors. To further demonstrate value in detecting anticancer therapy, we rank the drug pairs for each cell line based on model predicted combination effect and recover 80% of the top pairs with enhanced activity.ConclusionsWe present promising results in applying deep learning to predicting combinational drug response. Our feature analysis indicates screening data involving more cell lines are needed for the models to make better use of molecular features.
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