ArticleNAR genomics and bioinformatics2026
A comprehensive evaluation of self-attention for detecting regulatory feature interactions.
Article in NAR genomics and bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
The successful use of deep learning in computational biology depends on the ability to extract meaningful biological information from the trained models. Recent work has demonstrated that the attention maps generated by self-attention layers can be interpreted to predict cooperativity between binding of transcription factors, a key feature of gene regulatory networks. We extend this earlier work and demonstrate that the addition of an entropy term yields high-precision sparse attention maps that are easy to interpret. Furthermore, we performed a comprehensive evaluation of the relative performance of different flavors of attention-based transcription factor cooperativity discovery. Our findings demonstrate the benefit of the entropy-enhanced attention models and provide additional insights that would enable practitioners to make effective use of this valuable tool for biological discovery.
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