A Multi-Relational Graph Encoder Network for Fine-Grained Prediction of MiRNA-Disease Associations
Shengpeng Yu, Hong Wang, Jing Li, Jun Zhao, Cheng Liang, Yanshen Sun
Abstract
MicroRNAs (miRNAs) are critical in diagnosing and treating various diseases. Automatically demystifying the interdependent relationships between miRNAs and diseases has recently made remarkable progress, but their fine-grained interactive relationships still need to be explored. We propose a multi-relational graph encoder network for fine-grained prediction of miRNA-disease associations (MRFGMDA), which uses practical and current datasets to construct a multi-relational graph encoder network to predict disease-related miRNAs and their specific relationship types (upregulation, downregulation, or dysregulation). We evaluated MRFGMDA and found that it accurately predicted miRNA-disease associations, which could have far-reaching implications for clinical medical analysis, early diagnosis, prevention, and treatment. Case analyses, Kaplan-Meier survival analysis, expression difference analysis, and immune infiltration analysis further demonstrated the effectiveness and feasibility of MRFGMDA in uncovering potential disease-related miRNAs. Overall, our work represents a significant step toward improving the prediction of miRNA-disease associations using a fine-grained approach could lead to more accurate diagnosis and treatment of diseases.