Predicting in vitro drug sensitivity using random forests

Gregory Riddick, Hua Song, Susie Ahn, Jennifer Walling, Diego Borges-Rivera, Wei Zhang, Howard A. Fine

Research output: Contribution to journalArticle

79 Scopus citations

Abstract

Motivation: Panels of cell lines such as the NCI-60 have long been used to test drug candidates for their ability to inhibit proliferation. Predictive models of in vitro drug sensitivity have previously been constructed using gene expression signatures generated from gene expression microarrays. These statistical models allow the prediction of drug response for cell lines not in the original NCI-60. We improve on existing techniques by developing a novel multistep algorithm that builds regression models of drug response using Random Forest, an ensemble approach based on classification and regression trees (CART). Results: This method proved successful in predicting drug response for both a panel of 19 Breast Cancer and 7 Glioma cell lines, outperformed other methods based on differential gene expression, and has general utility for any application that seeks to relate gene expression data to a continuous output variable.

Original languageEnglish (US)
Article numberbtq628
Pages (from-to)220-224
Number of pages5
JournalBioinformatics
Volume27
Issue number2
DOIs
StatePublished - Jan 1 2011

ASJC Scopus subject areas

  • Statistics and Probability
  • Medicine(all)
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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    Riddick, G., Song, H., Ahn, S., Walling, J., Borges-Rivera, D., Zhang, W., & Fine, H. A. (2011). Predicting in vitro drug sensitivity using random forests. Bioinformatics, 27(2), 220-224. [btq628]. https://doi.org/10.1093/bioinformatics/btq628