Prediction of miRNA-Disease Associations Based on Hybrid Gated GNN and Multi-Data Integration

Yeqiang Wang, Sharen Yun, Yuchen Zhang, Xiujuan Lei

Abstract: It is well-established that miRNAs play a crucial role in the occurrence and development of diseases. Current miRNA-disease associations prediction research faces several challenges, including model bias due to data sparsity, information loss from overlooked complex relationships during feature fusion and insufficient capability of existing methods to capture the intricate relationships (between miRNAs, genes, lncRNAs and diseases), thereby limiting prediction accuracy. Based on hybrid gated GNN and multi-data fusion, a method (PMDGGM) for predicting miRNA-disease associations is proposed in this study. PMDGGM constructed seven similarity networks by comprehensively considering the relationships between miRNA and related genes, miRNA, lncRNA and diseases. It provides a solid foundation for feature fusion and information propagation. Subsequently, the method captures the complex relationships between heterogeneous nodes through a bilinear pooling layer and uses a gating mechanism to fuse multi-source heterogeneous features, thereby predicting miRNA-disease associations more accurately. The experimental results show that the method performs well and has significant advantages in predicting the miRNA-disease associations. Among various evaluation metrics, especially the AUCs of ROC and PR curves, the performance of method is outstanding, reaching a high level of 0.9413 and 0.9362. The study conducted case analyses on two diseases heart failure and acute myeloid leukemia. The predicted associated miRNAs can be validated by existing biomedical research efforts. The source code and data of PMDGGM can be publicly accessed on GitHub for further research and verification: https://github.com/WangYeQianger/PMDGGM