Fig. 3From: NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humansComparative performance of NCBoost models trained upon different sets of features. The figure represents the area under the receiver operating characteristic curve (AUROC; a) and the area under the precision-recall curve (AUPRC; b) obtained for each of the six feature configurations evaluated (feature categories A, B, A+B, A+B+C, A+B+C+D, and A+B+C+D+E) when tested mimicking a tenfold cross-validation on n = 283 high-confidence pathogenic non-coding SNVs and n = 2830 common variants without clinical assertionsBack to article page