Integrating thermodynamic and sequence contexts improves protein-RNA binding prediction.

ABSTRACT: Predicting RNA-binding protein (RBP) specificity is important for understanding gene expression regulation and RNA-mediated enzymatic processes. It is widely believed that RBP binding specificity is determined by both the sequence and structural contexts of RNAs. Existing approaches, including traditional machine learning algorithms and more recently, deep learning models, have been extensively applied to integrate RNA sequence and its predicted or experimental RNA structural probabilities for improving the accuracy of RBP binding prediction. Such models were trained mostly on the large-scale in vitro datasets, such as the RNAcompete dataset. However, in RNAcompete, most synthetic RNAs are unstructured, which makes machine learning methods not effectively extract RBP-binding structural pre

SUBMITTER: Su Y 

PROVIDER: S-EPMC6752863 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature