Learning Distance-Dependent Motif Interactions: An Explicitly Interpretable Neural Model of Genomic Events

In many biological studies, prediction is used primarily to validate the model; the real quest is to understand the underlying phenomenon. Therefore, interpretable deep models for biological studies are required. Here, we propose the Hyper-parameter eXplainable Motif Pair framework (HyperXPair) to model biological motifs and their distance-dependent context through explicitly interpretable parameters. This makes HyperXPair more than a decision-support tool; it is also a hypothesis-generating tool designed to advance knowledge in the field. We demonstrate the utility of our model by learning distance-dependent motif interactions for two biological problems: transcription initiation and RNA splicing.