Prediction of circRNA–miRNA Interaction Using Graph Attention Network Based on Molecular Attributes and Biological Networks

(1) Background: Circular RNAs (circRNAs) are covalently closed single-stranded molecules that play crucial roles in gene regulation, while microRNAs (miRNAs), specifically mature microRNAs, are naturally occurring small molecules of non-coding RNA with 17-25-nucleotide sizes. Understanding circRNA–miRNA interactions (CMIs) can reveal new approaches for diagnosing and treating complex human diseases. (2) Methods: In this paper, we propose a novel approach for predicting CMIs based on a graph attention network (GAT). We utilized DNABERT to extract molecular features of the circRNA and miRNA sequences and role-based graph embeddings generated by Role2Vec to extract the CMI features. The GAT’s ability to learn complex node dependencies in biological networks provided enhanced performance over the existing methods and the traditional deep neural network models. (3) Results: Our simulation studies showed that our GAT model achieved accuracies of 0.8762 and 0.8837 on the CMI-9905 and CMI-9589, respectively. These accuracies were the highest among the other existing CMI prediction methods. Our GAT method also achieved the highest performance as measured by the precision, recall, F1-score, area under the receiver operating characteristic (AUROC) curve, and area under the precision–recall curve (AUPR). (4) Conclusions: These results reflect the GAT’s ability to capture the intricate relationships between circRNAs and miRNAs, thus offering an efficient computational approach for prioritizing potential interactions for experimental validation.