Uncovering Regulatory Networks Through Residual Graph Learning of circRNA–miRNA Interactions

Circular RNAs (circRNAs) and microRNAs (miRNAs) are key regulators of gene expression, and their interactions are involved in many biological processes and diseases. Nevertheless, the experimental validation of circRNA–miRNA interactions (CMIs) remains challenging due to resource and technical constraints; therefore, robust computational models are essential for accurate CMI prediction. This paper proposes a new residual graph learning framework (RGLRE) for CMIs prediction using role‐aware graph embeddings. RGLRE combines attention‐based message transmission, DropEdge regularization, and similarity‐based hard negative sampling. Role2Vec embeddings are employed in RGLRE to provide role‐aware structural representations of circRNA and miRNA nodes, which support stable information propagation in the residual graph neural network. These design choices enable more robust and generalizable feature learning for CMI prediction. The experimental analysis of various datasets reveals that RGLRE is more effective in capturing complicated biological dependencies compared with the existing models like CMAGN, BEROLECMI, and GAT. RGLRE was evaluated on the CMI‐9905 and CMI‐9589 datasets with 5‐fold cross‐validation, and it showed better results than the current techniques. More precisely, the model showed accuracy of 0.9115 on CMI‐9905 and 0.9161 on CMI‐9589, and the overall score of the model consistently obtained high AUROC and AUPR values. The framework’s modular design and scalability make it a promising tool for broader biological interaction prediction tasks.