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Fig. 3 | Genome Biology

Fig. 3

From: NCBoost classifies pathogenic non-coding variants in Mendelian diseases through supervised learning on purifying selection signals in humans

Fig. 3

Comparative 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 assertions

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