Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions

Zichao Yan, William L. Hamilton, Mathieu Blanchette

Abstract: RNA-protein interactions are key effectors of post-transcriptional regulation. Significant experimental and bioinformatics efforts have been expended on characterizing protein binding mechanisms on the molecular level, and on highlighting the sequence and structural traits of RNA that impact the binding specificity for different proteins. Yet our ability to predict these interactions in silico remains relatively poor.