AUC-RF: A New Strategy for Genomic Profiling with Random Forest

Luz Calle, M.; Urrea, Victor; Boulesteix, Anne-Laure und Malats, Nuria (2011): AUC-RF: A New Strategy for Genomic Profiling with Random Forest. In: Human Heredity, No. 2: pp. 121-132

Abstract

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

• Item Type: Journal article
• Form of publication: Publisher's Version
• Faculties: Medicine > Institute for Medical Information Processing, Biometry and Epidemiology
• Subjects: 600 Technology > 610 Medicine and health
• ISSN: 0001-5652
• Alliance/National Licence: This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.
• Language: English