Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.27.270967v1?rss=1

Authors: Quinn, T. P., Nguyen, D., Nguyen, P., Gupta, S., Venkatesh, S.

Abstract:

In most 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 HyperXPair (the Hyper-parameter eXplainable Motif Pair framework), a new architecture that learns biological motifs and their distance-dependent context through explicitly interpretable parameters that are immediately understood by a biologist. 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.

Copy rights belong to original authors. Visit the link for more info