al., 2009; Mort et al., 2010] with a performance accuracy of 90%. However, the observed performances show that our weighted method once again performs favorably when compared to other state-of-the-art prediction methods: SNPs&GO [Calabrese et al., 2009], despite the domain-based restriction inherited from our pathogenicity weights. Next, we compared the performance of our unweighted method via receiver operating characteristic (ROC) curves against the top ranking theoretical/unweighted computational prediction methods: SIFT and PANTHER (Fig. 2A, B—see Supp. Fig. S3 for a comprehensive ROC curve against all evaluated methods). Impressively, given a 10% false positive rate, it seems that the performance of our unweighted method is comparable to SIFT thereby highlighting the sensitivity of our method to small fluctuations within the underlying amino acid probabilities. Furthermore, we compared the performance of our weighted method via ROC curves against the top-ranking trained/weighted prediction algorithms: MutPred and SNPs&GO (Fig. 2C, D). These results confirm that our weighted method performs favorably when compared to SNPs&GO.
