RNA-RBP interactions recognition using multi-label learning and feature attention allocation

Abstract

In this study, we present a sophisticated multi-label deep learning framework for the prediction of RNA-RBP (RNA-binding protein) interactions, a critical aspect in understanding RNA functionality modulation and its implications in disease pathogenesis. Our approach leverages machine learning to develop a rapid and cost-efficient predictive model for these interactions. The proposed model captures the complex characteristics of RNA and recognizes corresponding RBPs through its dual-module architecture. The first module employs convolutional neural networks (CNNs) for intricate feature extraction from RNA sequences, enabling the model to discern nuanced patterns and attributes. The second module is a multi-view multi-label classification system incorporating a feature attention mechanism. The second module is a multi-view multi-label classification system that utilizes a feature attention mechanism. This mechanism is designed to intricately analyze and distinguish between common and unique deep features derived from the diverse RNA characteristics. To evaluate the model's efficacy, extensive experiments were conducted on a comprehensive RNA-RBP interaction dataset. The results emphasize substantial improvements in the model's ability to predict RNA-RBP interactions compared to existing methodologies. This advancement emphasizes the model's potential in contributing to the understanding of RNA-mediated biological processes and disease etiology.

Deep Learning for Elucidating Modifications to RNA—Status and Challenges Ahead

“…A popular alternative representation is using k-mers, whereby the sequence is broken up into overlapping segments of length k, before often being collapsed into a vector of counts for each possible sequence. On the whole, there is a trend towards using wider sequence contexts around positions of interest, with some even including full transcript sequences [33,56], although note that with larger models there may be a trade-off between the maximum input sequence length and the availability of GPU capacity for training. Interestingly, recent deep learning models for RBP appear to show that sequence alone can achieve high performance scores for determining the binding status for a large number of RBPs [29,34,43,57], the locations of m 6 A sites [33], and A-to-I editing sites [51].…”