in our independent benchmark was collected from the SwissVar [Mottaz et al., 2010] portal. As a result, the estimated performances of other computational prediction methods which have been trained on SwissProt/TrEMBL mutations may be overinflated. Here, MutPred is the best performing method; however, the performance of our weighted method is comparable to SNPs&GO. To alleviate the potential bias in our method, we performed a leave-one-out analysis and found no significant deviations in the observed performances; we therefore concluded that the performances observed in FATHMM were not an artifact of the weighting scheme employed. The performances of all trained/weighted computational prediction algorithms were, somewhat surprisingly, inferior when compared to their theoretical/unweighted counterparts across four cancer-associated genes. The performances observed within Align-GVGD (gene-specific alignments) suggest that there may be some benefit in incorporating disease-specific weightings into our algorithm, for example, cancer-specific weightings similar to those employed by Capriotti and Altman (2011).
