iLDA ‐ SGCN : Identifying Associations Between Age‐Related Diseases and Long Non‐Coding RNAs Using Dual Graph Convolutional Networks

Aging reshapes global disease burdens, yet the regulatory roles of long non-coding RNAs (lncRNAs) in age-related disorders remain incompletely characterized. We developed iLDA-SGCN, a graph-based computational framework that integrates singular value decomposition (SVD) with dual graph convolutional networks (GCNs) to predict lncRNA-disease associations. SVD first derives compact low-dimensional representations from the lncRNA-disease association matrix. Two complementary GCN modules then learn topology-aware embeddings: a correlation-map GCN operating on the bipartite lncRNA-disease network, and a similarity-map GCN operating on fused homogeneous graphs of lncRNAs and diseases constructed from MeSH semantic similarity and Gaussian association-profile kernels. Finally, association scores are estimated with an inner-product decoder optimized with a class-imbalance-aware loss function. Across five-fold cross-validation on LncRNADisease and MNDR datasets, iLDA-SGCN outperformed five competitive methods (SDLDA, LDNFSGB, IPCARF, LDASR, and LDA-VGHB) in terms of AUC (area under the ROC curve) and AUPR (area under the precision-recall curve). The model achieved AUC/AUPR of 0.960/0.968 on MNDR and 0.896/0.901 on LncRNADisease, with only a marginal precision shortfall versus LDA-VGHB on LncRNADisease. Ablation studies showed both GCN modules improved over a fully connected backbone, with the similarity-map GCN contributing the largest gains; the full model performed best overall. In case studies across eight prototypical age-related diseases, iLDA-SGCN identified HOTAIR, MALAT1, PVT1, MEG3, H19, LSINCT5, UCA1, and other candidates, yielding 33 candidates potentially involved in age-related disease mechanisms that require further experimental validation. Collectively, iLDA-SGCN integrates semantic and topological information to prioritize candidate lncRNA-disease associations related to aging, providing testable hypotheses for downstream mechanistic studies.