A deep learning framework for modeling structural features of RNA-binding protein targets.

ABSTRACT: RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework co

SUBMITTER: Zhang S 

PROVIDER: S-EPMC4770198 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature