A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.

ABSTRACT: RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher per

SUBMITTER: Jain DS 

PROVIDER: S-EPMC6015049 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature