To understand the potential complementarity/redundancy of FATHMM to other methods, we assessed the intersection of disease-associated AASs correctly identified (true positives) by our method and the top-ranking computational prediction algorithms (Fig. 3). From this analysis, it was clear that no one method completely encapsulates all other methods that is, each method succeeded in correctly and uniquely identifying some disease-associated AASs where other methods did not. These results reaffirm previous suggestions that combining predictions from multiple prediction methods has the potential to perform better than any individual method [Liu et al., 2011; Olatubosun et al., 2012].
