Fig. 2

Importance of feature sets. a We test the importance of related sets of features by excluding one set of features from the training data, measuring the performance (AUC-PR) of the resulting classifier, and subtracting this AUC-PR value from the corresponding value achieved by the classifier using all features. Hence, if Δ AUC-PR is above zero, the left-out set of features improved the final prediction performance, whereas Δ AUC-PR values below zero indicate a negative effect on prediction performance. We collect the Δ AUC-PR values for all 13 test data sets and visualize these as violin plots. b Assessment of different groups of DNase-seq-based features. In this case, we compare the performance including one specific group of DNase-seq-based features (cf. Additional file 1: Text S2)) with the performance without any DNase-seq-based features (cf. violin “DNase-seq” in panel a). We find that all DNase-seq-based features contribute positively to prediction performance