Skip to main content
Fig. 3 | Genome Biology

Fig. 3

From: Chromatin interaction neural network (ChINN): a machine learning-based method for predicting chromatin interactions from DNA sequences

Fig. 3

Sequence feature importance scores of gradient boosted trees trained on extended datasets. The “Pol2” in the figure represents “RNA Pol II”. a, b The importance scores of sequence features extracted from both directions (F, forward; RC, reverse complement) of the two anchors (left and right) by models trained on different datasets. The orange horizontal lines indicate average importance scores of the features from the strand of the anchor. c Pearson correlations between feature importance scores of the two anchors. d The importance scores of sequence features extracted from both directions (F, forward; RC, reverse complement) of the two anchors (left and right) by models trained on Hi-C datasets. The orange horizontal lines indicate average importance scores of the features from the strand of the anchor. e Pearson correlations between feature importance scores of the two anchors in Hi-C datasets

Back to article page