ROC (receiver operating characteristic) is a generally applicable, non-parametric procedure that provides insight into the discriminatory properties of data features for a binary classifier. However, the method is not efficient for gene expression experiments as they generally do not produce a sufficient number of samples. bROC overcomes that limitation by resampling (bootstrapping) the expression data to produce a large number of simulated measurements that preserve the statistical properties of the original data.

Thus, bROC can produce detailed curves of sensitivity (probability of true positive detection) vs. 1-specificity (probability of false positive detection) for all features of interest. CONF = 2 AUC - 1, where AUC is the area under ROC curve, is the primary statistics used for detection of regulated features (probes/genes).