We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Predicting in vitro drug sensitivity using Random Forests.
- Authors
Riddick, Gregory; Song, Hua; Ahn, Susie; Walling, Jennifer; Borges-Rivera, Diego; Zhang, Wei; Fine, Howard A
- Abstract
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).
- Publication
Bioinformatics (Oxford, England), 2011, Vol 27, Issue 2, p220
- ISSN
1367-4811
- Publication type
Journal Article
- DOI
10.1093/bioinformatics/btq628