A Hybrid Deep Neural Network Utilizing Graph Convolution for thePrediction of CircRNA-RBP Interaction

Introduction: CircRNA, with its covalently closed circular structure, plays key roles in biological functions and diseases by interacting with RNA-binding proteins (RBPs) and microRNAs (miRNAs). However, existing computational methods struggle to capture secondary structure features. Methods: We introduce CSGN, a graph neural network model that predicts circRNA-RBP interactions using secondary structure information. CSGN enhances physicochemical feature encodings by incorporating pseudo-secondary structures from thermodynamic models and utilizes graph convolutional networks (GCNs) for feature extraction. It also integrates Doc2Vec embeddings and employs CNNs, BiGRUs, and MLPs for efficient feature representation. Results: CSGN outperforms existing models across 16 datasets. Ablation studies confirm the significance of RNA secondary structure and GCNs in improving prediction accuracy. Principal component analysis further highlights CSGN's strength in feature extraction. Discussion: CSGN advances circRNA-RBP prediction by integrating GCNs and Doc2Vec, though global structural constraints remain. Future work should address longer-sequence modeling and experimental validation. Conclusion: CSGN effectively improves circRNA-RBP interaction prediction, demonstrating superior performance through the integration of RNA secondary structure and GCNs.