RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.

ABSTRACT: RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protei

SUBMITTER: Shen WJ 

PROVIDER: S-EPMC6017498 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature