Our studies revealed that: (i) the depth of conservation of the mutated residue is a useful, novel feature for predicting cancer-associated mutations; (ii) combining multiple classifiers can improve prediction accuracies; and (iii) our novel features and multiple classifiers can be effectively applied in the identification of rare mutations in EGFR. The gene discussed is EGFR; the disease is cancer.