ETNet: an interpretable transformer framework for enhancer–enhancer interaction prediction with cross-context transferability

Enhancer–enhancer interactions (EEIs) are critical regulatory components in transcriptional networks but remain computationally challenging to predict. While enhancer–promoter interactions have been extensively studied, EEIs remain comparatively underexplored. We developed ETNet (Enhancer–enhancer Interaction Explainable Transformer Network), a deep learning architecture integrating convolutional neural networks with Transformer modules to predict EEIs from DNA sequences. Evaluation across three cell lines (GM12878, K562, MCF-7) demonstrated superior performance compared to existing methods including EnContact, with statistical significance confirmed through DeLong tests across six cell lines. Rigorous validation through cross-validation and enhancer-level data partitioning confirmed robust generalization. ETNet exhibited effective cross-cell type transfer learning and showed transferability to enhancer–promoter interaction tasks, providing exploratory evidence for shared chromatin interaction principles. Feature attribution analysis recovered cell-type-specific regulatory motifs consistent with known transcription factors and revealed computational evidence for super-additive cooperative mechanisms, with cooperativity negatively correlating with sequence similarity—patterns representing hypothesis-generating observations requiring experimental validation. Proof-of-concept analysis demonstrated how single-nucleotide polymorphisms in JAK–STAT pathway genes may influence predicted interactions through motif alterations. ETNet advances computational approaches for studying enhancer interactions and provides a framework combining predictive capability with exploratory interpretability.