Journal
HUMAN HEREDITY
Volume 72, Issue 2, Pages 121-132Publisher
KARGER
DOI: 10.1159/000330778
Keywords
AUC; Gene identification; Genomic profile; Random forest; ROC curve; Variable selection
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Funding
- Ministerio de Educacion y Ciencia (Spain) [MTM2008-06747-C02-02]
- Marato de TV3 Foundation [050831]
- AGAUR-Generalitat de Catalunya [2009SGR-581]
- LMU-innovativ Project BioMed-S
- Spanish Ministry of Education
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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
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