Fig. 2

Architecture and performances of the ChINN sequence-based models on distance-matched datasets. The “Pol2” in the figure represents “RNA Pol II”. a The architecture of the sequence-based models using to train on distance-matched datasets. Precision-recall curves of the sequence-based models on distance-matched datasets using b only sequence features or c sequence features with distance. The numbers in the brackets indicates the area-under precision-recall curves. Across-sample performances as measured by area-under precision-recall curve (auPRC) of the models on distance-matched datasets using d only sequence features or e sequence features with distance. Precision-recall curves of the sequence-based models on distance-matched Hi-C datasets using f only sequence features or g sequence features with distance. The numbers in the brackets indicates the area-under precision-recall curves. Across-sample performances as measured by area-under precision-recall curve (auPRC) of the models on distance-matched Hi-C datasets using h only sequence features or i sequence features with distance