EpiXFormer: a cross-attention neural network for predicting cell type-specific transcription factor binding sites.

ABSTRACT: Transcription factors (TFs) bind to specific sequences in the genome to regulate gene expression and specify cell states. TF binding sites (TFBSs) are cell type-specific, which can be attributed to epigenomic contexts. Comprehensive profiling of TFBSs across various cell types through experimental approaches is neither practical nor cost-friendly. Accurately identifying cell type-specific TFBSs through computational approaches remains challenging. Here, we develop EpiXFormer, a novel transformer-based neural network for cell type-specific TFBS prediction. EpiXFormer achieves exceptional performance in predicting binding sites of DNA-binding proteins (DBPs) across a diverse collection of cell types. It models the effects of proximal and distal epigenomic information on DBP binding and learn

SUBMITTER: Peng Y 

PROVIDER: S-EPMC12796812 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature