AUC-RF: A New Strategy for Genomic Profiling with Random Forest
AUC-RF: A New Strategy for Genomic Profiling with Random Forest
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DOI：
10.1159/000330778
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发表时间：
2011-01-01
期刊：
影响因子：
1.8
通讯作者：
Malats, Nuria
中科院分区：
文献类型：
--
作者：
Luz Calle, M.;Urrea, Victor;Malats, Nuria
目的：基因组分析，同时使用多个基因座的遗传变异来预测疾病风险，需要选择一组最能预测疾病状态的遗传变异。这项工作的目标是提供一个新的选择算法的基因组图谱。研究方法：我们提出了一种新的算法，基因组分析的基础上优化的随机森林（RF）的受试者工作特征曲线（AUC）下的面积。所提出的策略实现了一个向后淘汰过程的基础上的初始排名的变量。结果和结论：我们证明了使用AUC代替分类误差作为RF预测准确性的度量的优势。特别是，我们表明，使用的分类错误是特别不适当的，当处理不平衡的数据集。变量选择和预测的新程序，即AUC-RF，说明了从膀胱癌研究的数据，也与模拟数据。该算法作为一个名为AUCRF的R包在http://cran.r-project.org/上公开提供。版权所有（C）2011 S. Karger AG，巴塞尔
Objective: Genomic profiling, the use of genetic variants at multiple loci simultaneously for the prediction of disease risk, requires the selection of a set of genetic variants that best predicts disease status. The goal of this work was to provide a new selection algorithm for genomic profiling. Methods: We propose a new algorithm for genomic profiling based on optimizing the area under the receiver operating characteristic curve (AUC) of the random forest (RF). The proposed strategy implements a backward elimination process based on the initial ranking of variables. Results and Conclusions: We demonstrate the advantage of using the AUC instead of the classification error as a measure of predictive accuracy of RF. In particular, we show that the use of the classification error is especially inappropriate when dealing with unbalanced data sets. The new procedure for variable selection and prediction, namely AUC-RF, is illustrated with data from a bladder cancer study and also with simulated data. The algorithm is publicly available as an R package, named AUCRF, at http://cran.r-project.org/. Copyright (C) 2011 S. Karger AG, Basel