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공공누리This item is licensed Korea Open Government License

Title
A community effort to assess and improve drug sensitivity prediction algorithms
Author(s)
JamesCCostello이준학
Publication Year
2014-06-01
Abstract
Predicting the best treatment strategy from genomic information is a core goal of precision medicine. Here we focus on predicting drug response based on a cohort of genomic, epigenomic and proteomic profiling data sets measured in human breast cancer cell lines. Through a collaborative effort between the National Cancer Institute (NCI) and the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we analyzed a total of 44 drug sensitivity prediction algorithms. The top-performing approaches modeled nonlinear relationships and incorporated biological pathway information. We found that gene expression microarrays consistently provided the best predictive power
of the individual profiling data sets, however, performance
was increased by including multiple, independent data sets.
We discuss the innovations underlying the top-performing methodology, Bayesian multitask MKL, and we provide detailed descriptions of all methods. This study establishes benchmarks for drug sensitivity prediction and identifies approaches that can be leveraged for the development of new methods.
Keyword
drug sensitivity; data mining; machine learning; cancer
Journal Title
Nature biotechnology
ISSN
1087-0156
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Appears in Collections:
7. KISTI 연구성과 > 학술지 발표논문
URI
https://repository.kisti.re.kr/handle/10580/14362
http://www.ndsl.kr/ndsl/search/detail/article/articleSearchResultDetail.do?cn=NART71086638
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