NDLI: An Integrated Approach to Anti-Cancer Drug Sensitivity Prediction

Content Provider	IEEE Xplore Digital Library
Author	Berlow, N. Haider, S. Qian Wan Geltzeiler, M. Davis, L.E. Keller, C. Pal, R.
Copyright Year	2004
Abstract	A framework for design of personalized cancer therapy requires the ability to predict the sensitivity of a tumor to anticancer drugs. The predictive modeling of tumor sensitivity to anti-cancer drugs has primarily focused on generating functions that map gene expressions and genetic mutation profiles to drug sensitivity. In this paper, we present a new approach for drug sensitivity prediction and combination therapy design based on integrated functional and genomic characterizations. The modeling approach when applied to data from the Cancer Cell Line Encyclopedia shows a significant gain in prediction accuracy as compared to elastic net and random forest techniques based on genomic characterizations. Utilizing a Mouse Embryonal Rhabdomyosarcoma cell culture and a drug screen of 60 targeted drugs, we show that predictive modeling based on functional data alone can also produce high accuracy predictions. The framework also allows us to generate personalized tumor proliferation circuits to gain further insights on the individualized biological pathway.
Sponsorship	IEEE Computer Society
Starting Page	995
Ending Page	1008
Page Count	14
File Size	1432997
File Format	PDF
ISSN	15455963
Volume Number	11
Issue Number	6
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-11-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Drugs Cancer Bioinformatics Tumors Genomics Predictive models Biomedical signal processing Statistical analysis Computational biology personalized cancer therapy Drug sensitivity prediction
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

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