Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/6847
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
Forecasting
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.
URI: http://doi.org/10.1038/nbt.2877
http://hdl.handle.net/11147/6847
ISSN: 1546-1696
1087-0156
Appears in Collections:Electrical - Electronic Engineering / Elektrik - Elektronik Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Files in This Item:
File Description SizeFormat 
6847.pdfMakale3.13 MBAdobe PDFThumbnail
View/Open
Show full item record

CORE Recommender

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.