A Comprehensive Evaluation of Self Attention for Detecting Regulatory Feature Interactions

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 sparser attention maps that are easier to interpret and provide higher precision interpretations. Furthermore, we performed a comprehensive evaluation of the relative performance of different flavors of attention-based transcription factor cooperativity discovery, and compared methods that use raw attention scores to the use of attribution over the attention scores. 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.