EPINTLM: enhancer-promoter prediction with pretrained k-mer embeddings and residual cross-attention.

ABSTRACT: Enhancer-promoter interactions (EPIs) play an important role in gene regulation, yet experimental mapping remains costly and limited in coverage. As a result, computational approaches are commonly evaluated under curated benchmark datasets, which pose challenges related to long-range sequence modeling, multimodal feature integration, and reproducible preprocessing. In this study, we present EPINTLM (Enhancer-Promoter Interaction Nucleotide Transformer Large Model), a deep learning framework designed to investigate architectural strategies for EPI prediction under standardized benchmark settings. EPINTLM integrates DNA sequence representations and genomic features by leveraging pretrained k-mer embeddings from the Nucleotide Transformer and explicitly modeling intra- and inter-sequence depe

SUBMITTER: Nguyen TL 

PROVIDER: S-EPMC12908682 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature