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A community effort to assess and improve drug sensitivity prediction algorithms

(2014) NATURE BIOTECHNOLOGY. 32(12). p.1202-1212
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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.
Keywords
ENCYCLOPEDIA, IDENTIFICATION, ARRAY, GENES, BREAST-CANCER, CANCER CELL-LINES, RESPONSES, SIGNATURES, INFERENCE, DISCOVERY

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Chicago
Costello, James C, Laura M Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P Menden, Nicholas J Wang, Mukesh Bansal, et al. 2014. “A Community Effort to Assess and Improve Drug Sensitivity Prediction Algorithms.” Nature Biotechnology 32 (12): 1202–1212.
APA
Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., et al. (2014). A community effort to assess and improve drug sensitivity prediction algorithms. NATURE BIOTECHNOLOGY, 32(12), 1202–1212.
Vancouver
1.
Costello JC, Heiser LM, Georgii E, Gönen M, Menden MP, Wang NJ, et al. A community effort to assess and improve drug sensitivity prediction algorithms. NATURE BIOTECHNOLOGY. 2014;32(12):1202–12.
MLA
Costello, James C et al. “A Community Effort to Assess and Improve Drug Sensitivity Prediction Algorithms.” NATURE BIOTECHNOLOGY 32.12 (2014): 1202–1212. Print.
@article{5950248,
  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.},
  author       = {Costello, James C and Heiser, Laura M and Georgii, Elisabeth and G{\"o}nen, Mehmet and Menden, Michael P and Wang, Nicholas J and Bansal, Mukesh and Ammad-ud-din, Muhammad and Hintsanen, Petteri and Khan, Suleiman A and Mpindi, John-Patrick and Kallioniemi, Olli and Honkela, Antti and Aittokallio, Tero and Wennerberg, Krister and Collins, James J and Gallahan, Dan and Singer, Dinah and Saez-Rodriguez, Julio and Kaski, Samuel and Gray, Joe W and Stolovitzky, Gustavo and NCI DREAM Communicty, the and De Baets, Bernard and Stock, Michiel and Waegeman, Willem},
  issn         = {1087-0156},
  journal      = {NATURE BIOTECHNOLOGY},
  language     = {eng},
  number       = {12},
  pages        = {1202--1212},
  title        = {A community effort to assess and improve drug sensitivity prediction algorithms},
  url          = {http://dx.doi.org/10.1038/nbt.2877},
  volume       = {32},
  year         = {2014},
}

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