Learning Distance-Dependent Motif Interactions: An Explicitly Interpretable Neural Model of Genomic Events
Thomas P. Quinn, Dang Nguyen, Phuoc Nguyen, Sunil Gupta, Svetha Venkatesh
The paper introduces HyperXPair, an interpretable neural model that learns distance-dependent motif interactions for transcription initiation and RNA splicing.
bioRxiv · Aug 27, 2020 · PreprintBioinformatics