Analyzing drug sensitivity prediction based on dose response curve characteristics

Raziur Rahman, Ranadip Pal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

Precision medicine for cancer involves design of drug sensitivity prediction models that can predict patient response to various drugs. The drug response is usually represented by a single feature such as Area Under the Curve or IC50 derived from the experimental dose response curve. In this article, we consider the idea that predicting the dose response curve and generating the curve features instead of directly predicting the curve characteristics can increase prediction accuracy. Using the cancer cell line encyclopedia database, we illustrate that predicting dose response curve points to calculate AUC instead of directly predicting AUC can reduce prediction mean square error and increase correlation between experimental and predicted values.

Original languageEnglish
Title of host publication3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages140-143
Number of pages4
ISBN (Electronic)9781509024551
DOIs
StatePublished - Apr 18 2016
Event3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States
Duration: Feb 24 2016Feb 27 2016

Publication series

Name3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016

Conference

Conference3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
CountryUnited States
CityLas Vegas
Period02/24/1602/27/16

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