Fig. 1
From: Comprehensive assessment of computational algorithms in predicting cancer driver mutations
Feature summary and inter-correlations between algorithms. a Based on features included, each algorithm was labeled as using ensemble score, sequence context, protein feature, conservation, or epigenomic information. The algorithms trained on cancer diver data or proposed to identify cancer drivers are labeled as cancer-specific. b Left: hierarchical clustering pattern of 33 algorithms based on ~ 710,000 TCGA somatic mutations; right, a triangle heatmap displays the Spearman rank correlation coefficient between any two algorithms