Densely Convolutional Neural Network for Transcription Factor Binding Sites Prediction Using DNA Sequence and Histone Modification

Transcription factor binding sites (TFBSs) prediction is crucial for decoding cis-regulation. However, current deep learning methods fail to simultaneously consider the multiscale features from DNA sequences and histone modifications in an efficient manner. To this end, we propose a novel D ensely C onvolutional N eural N etwork using DNA S equence and H istone Modification, dubbed as DCNN-SH, for TFBSs prediction. Our model adopts densely convolutional blocks to reuse multi-length motifs and multi-order dependencies of nucleotides. This unique design allows our model to consider the multi-scale features using smaller convolutional kernels compared to current methods. Our work is the first to apply densely network for TFBSs prediction. Extensive experiments over 300 ChIP-seq datasets demonstrate that our model significantly outperforms several state-of-the-art prediction methods in terms of accuracy, ROC-AUC and PR-AUC.