Incorporating graph representation and mutual attention mechanism for MiRNA-MRNA interaction prediction.

Introduction: Predicting interactions between microRNAs (miRNAs) and messenger RNAs (mRNAs) is crucial for understanding gene expression regulation mechanisms and their roles in diseases. Existing prediction methods face significant limitations in simultaneously handling RNA sequence complexity and graph structural information. Methods: We propose GRMMI, a framework that effectively leverages both sequence and node features by combining FastText-pretrained sequence embeddings with GraRep graph embeddings to capture semantic and topological information. The method introduces antisense-aware sequence processing that reverses mRNA orientation to better simulate the natural miRNA-mRNA complementary binding mechanism. Additionally, GRMMI employs cross-sequence mutual attention architecture that enables deep exploration of inter-RNA dependencies beyond traditional single-sequence analysis limitations. Unlike existing approaches that rely primarily on sequence-based features, GRMMI achieves multi-dimensional information fusion by integrating CNN-BiLSTM architecture with mutual attention mechanisms. Results: Evaluation on the MTIS-9214 dataset shows that GRMMI achieves an AUC of 0.9347 and accuracy of 86.65%. Discussion: Case studies confirm the practical utility of GRMMI in identifying biologically significant RNA interactions, providing valuable insights for disease mechanism research and therapeutic target discovery.