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Title: A community effort to assess and improve drug sensitivity prediction algorithms
Authors: Costello, James C.
Heiser, Laura M.
Georgii, Elisabeth
Gönen, Mehmet
Menden, Michael P.
Wang, Nicholas J.
Bansal, Mukesh
Ammad-ud-din, Muhammad
Hintsanen, Petteri
Khan, Suleiman A.
Mpindi, John-Patrick
Kallioniemi, Olli
Honkela, Antti
Aittokallio, Tero
Wennerberg, Krister
NCI-DREAM Community
Karaçalı, Bilge
Collins, James J.
Gallahan, Dan
Singer, Dinah
Saez-Rodriguez, Julio
Kaski, Samuel
Gray, Joe W.
Stolovitzky, Gustavo
Keywords: Gene expression
Computational models
Biological pathways
Genomic information
Issue Date: Dec-2014
Publisher: Nature Publishing Group
Source: Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., and Wang, N. J., ...Stolovitzky, G. (2014). A community effort to assess and improve drug sensitivity prediction algorithms. Nature Biotechnology, 32(12), 1202-1212. doi:10.1038/nbt.2877
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.
ISSN: 1546-1696
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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