title: AUC-RF: a new strategy for genomic profiling with random forest.
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.
  
  
  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/.
authors: Systems Biology Department, University of Vic, Spain. malu.calle @ uvic.cat
authors: M Luz
authors: ML
authors: Calle
authors: Victor
authors: V
authors: Urrea
authors: Anne-Laure
authors: AL
authors: Boulesteix
authors: Nuria
authors: N
authors: Malats
mesh_terms: D000465:Algorithms; D019540:Area Under Curve; D019295:Computational Biology; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020407:Internet; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D015203:Reproducibility of Results; D012307:Risk Factors; D012984:Software; D001749:Urinary Bladder Neoplasms / Q000235:genetics