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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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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.
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.
Chicago author-date
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.
Chicago author-date (all authors)
Costello, James C, Laura M Heiser, Elisabeth Georgii, Mehmet Gönen, Michael P Menden, Nicholas J Wang, Mukesh Bansal, Muhammad Ammad-ud-din, Petteri Hintsanen, Suleiman A Khan, John-Patrick Mpindi, Olli Kallioniemi, Antti Honkela, Tero Aittokallio, Krister Wennerberg, James J Collins, Dan Gallahan, Dinah Singer, Julio Saez-Rodriguez, Samuel Kaski, Joe W Gray, Gustavo Stolovitzky, the NCI DREAM Communicty, Bernard De Baets, Michiel Stock, and Willem Waegeman. 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.
IEEE
[1]
J. C. Costello et al., “A community effort to assess and improve drug sensitivity prediction algorithms,” NATURE BIOTECHNOLOGY, vol. 32, no. 12, pp. 1202–1212, 2014.
@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 Gönen, 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},
  keywords     = {ENCYCLOPEDIA,IDENTIFICATION,ARRAY,GENES,BREAST-CANCER,CANCER CELL-LINES,RESPONSES,SIGNATURES,INFERENCE,DISCOVERY},
  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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