Predicting in vitro drug sensitivity using Random Forests
Predicting in vitro drug sensitivity using Random Forests
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DOI：
10.1093/bioinformatics/btq628
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发表时间：
2011-01-15
期刊：
影响因子：
5.8
通讯作者：
Fine, Howard A.
中科院分区：
文献类型：
--
作者：
Riddick, Gregory;Song, Hua;Fine, Howard A.
动机：长期以来，NCI-60等细胞系一直被用于测试候选药物抑制增殖的能力。体外药物敏感性的预测模型先前已经使用从基因表达微阵列产生的基因表达特征来构建。这些统计模型允许预测原始NCI-60中没有的细胞系的药物应答。我们通过开发一种新的多步算法来改进现有技术，该算法使用随机森林（一种基于分类和回归树（CART）的集成方法）构建药物反应的回归模型。该方法在预测一组19种乳腺癌和7种神经胶质瘤细胞系的药物反应方面被证明是成功的，优于基于差异基因表达的其他方法，实现：软件用R语言编写，并将与相关的基因表达和药物反应数据一起作为ivDrug软件包在www.example.com上提供http://r-forge.r-project.org。
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.Implementation: Software was written in the R language and will be available together with associated gene expression and drug response data as the package ivDrug at http://r-forge.r-project.org.