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

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