String-Based Models for Predicting RNA-Protein Interaction

Abstract

In this work, we study string-based approaches for the problem of RNA-Protein Interaction (RPI). We apply string algorithms and data structures to extract effective string patterns for prediction of RPI, using both sequence information (protein and RNA sequences), and structure information (protein and RNA secondary structures). This led to different string-based models for predicting interacting RNA-protein pairs. We show results that demonstrate the effectiveness of the proposed string-based models, including comparative results against state-of-the-art methods.