An important issue to consider when comparing the performance of trained/weighted computational prediction methods is the cross-validation dataset, that is, these prediction methods should ideally be tested using “blind” datasets to minimize the bias in the performances observed. Unfortunately, this level of testing is not possible as it would require retraining/validating all prediction methods with common datasets. However, the majority of disease-associated AASs in the VariBench database were collected from Locus-Specific Databases (LSDB) and are not found in commonly used training datasets, for example, SwissProt/TrEMBL [Apweiler et al., 2004]. Therefore, the curators claim this bias is minimized in this dataset [Thusberg et al., 2011]. Here, the performance of our weighted method appears to outperform the current state-of-the-art prediction methods: MutPred [Li et al., 2009; Mort et al., 2010] and SNPs&GO [Calabrese et al., 2009]. By contrast, the mutation dataset used 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
