Supplementary Figure 4: S-CAP performance in the high-sensitivity region.
From: S-CAP extends pathogenicity prediction to genetic variants that affect RNA splicing
The size of training and testing data for each model is specified in Table 3. The hsr-AUC curve is formed by subsetting the overall AUC to just the region where pathogenic variants are correctly classified over 95% of the time. An hsr-AUC curve is calculated for each of the regions as defined in Fig. 1c. S-CAP improves on the next best method’s hsr-AUC by 185% in the 3′ intronic region (a), 40.7% in the 3′ core sites (b), 224% in the exonic region (c), 31.6% in the 5′ core sites (d), 72.7% in the 5′ extended region (e) and 120% in the 5′ intronic region (f). None of the existing methods consistently outperforms the others.
