RGCNMDA: pathway-bridged relational graph learning with adaptive multi-view fusion for human miRNA-disease
Chao Hou1, Mohamed Kone1, Yang Xiang1
1College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan 410082, China.
Motivation:
MicroRNAs are key post-transcriptional regulators whose dysregulation is associated with complex human diseases. Computational prediction can prioritize candidate miRNA-disease associations, but reliable evaluation is complicated by sparse labels, cold-start entities, limited biological context in bipartite graphs, and leakage when association-derived features are constructed before data splitting.
Results:
We present RGCNMDA, a leakage-controlled multi-view framework that integrates global latent structure, local profiles and similarities, and pathway context. Within every fold, interaction profiles, GIP similarities, PCA inputs, MDMF factors, and miRNA-disease graph edges are reconstructed exclusively from training positives. Four independent factorized encoders transform the miRNA and disease interaction profiles and GIP similarities, while a fold-local MDMF branch captures global latent structure. These representations are integrated with a pathway-bridged graph containing miRNA, disease, and pathway nodes connected by six directed relation types. A relational graph convolutional network performs type- and direction-specific message passing, and node-wise gates adaptively fuse graph, MDMF, and combined profile and similarity representations before an MLP pair decoder scores candidate associations. On HMDD v4.0, RGCNMDA achieved AUCs of 0.9589, 0.9109, and 0.8836 under random, cold-disease, and cold-miRNA evaluation, respectively; on the processed independent-source RNADisease v4.0 benchmark, the corresponding values were 0.9580, 0.8446, and 0.8913. Same-protocol baseline comparisons and diagnostic analyses showed that the benefits of RGCNMDA were setting dependent, with the strongest pathway-related improvement under cold-disease evaluation. These results support the robustness of leakage-controlled multi-view learning across standard and cold-start evaluation settings.
Availability And Implementation:
https://github.com/hnuchao/pathway-RGCNMDA.
