Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism

Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-cell ATAC-seq data and embedding attention mechanisms needs to be improved. To this end, we present IscPAM, an interpretable method based on deep learning with an attention mechanism to predict single-cell transcription factors. Our model adopts …