RNA-Protein Interaction Networks

Interactions between RNA molecules and other RNAs or proteins are critical for understanding the complexity of cellular processes such as gene regulation, splicing, translation, and signal transduction. RNA-RNA and RNA-protein interaction networks map the physical and functional relationships between diverse RNA molecules and their interacting RNA or protein partners through graph structures, with the ultimate goal of predicting these interactions using network analysis. Recent advancements in high-throughput experimental techniques (e.g., CLIP-seq, RIP-seq, and RNA-RNA interaction detection) combined with sophisticated computational methods (e.g., graph neural networks and machine learning models) have significantly improved the accuracy and coverage of network construction and relationship prediction.

This topic focuses on state-of-the-art approaches for predicting RNA-RNA and RNA-protein interactions using network or graph structures. These approaches include high-throughput sequencing technologies, graph neural network-based prediction models, and integrative strategies that combine sequence data, RNA secondary structures, chemical modifications, and protein domain information. Particular emphasis is placed on leveraging graph embedding techniques and network topology analysis to uncover potential interaction patterns for high-precision relationship prediction. Furthermore, connections with network bioinformatics highlight graph algorithms for visualizing and analyzing complex interaction networks, while links to protein bioinformatics elucidate how protein structural features influence RNA binding specificity and prediction accuracy.

By constructing and analyzing RNA-RNA and RNA-protein interaction networks, we can not only uncover the molecular basis of normal cellular functions and disease mechanisms but also provide critical support for predicting novel interactions and developing therapeutic interventions targeting RNA complexes.