Fig. 2

Assessment using a benchmark dataset based on mutation 3D clustering pattern. a Overview of the assessment process. We used four computational algorithms to detect whether mutations are located within the protein 3D structural hotspots, each algorithm with one vote. The number of votes was defined as the consensus cluster score. A mutation with a score of ≥ 2 and in a cancer gene (i.e., cancer gene consensus) was considered as a positive case, and a mutation with a score of 0 and in a non-cancer gene was considered as a negative case. b ROC curves and corresponding AUC scores for the top 10 algorithms. c Boxplots showing the differences of AUC between two groups of algorithms with or without certain features. p value is based on the Wilcoxon rank sum test. d Sensitivity and specificity of each algorithm calculated by using the median score value as the threshold to make binary predictions. Error bars, mean ± 2SD