GMFLDA: Improved Prediction of lncRNA-Disease Association via Graph Convolutional Network

Abstract

The rapid expansion of diverse networks has created a growing need to integrate multiple heterogeneous structures to effectively capture both inter- and intra-entity relationships. This integration helps preserve the intrinsic meaning of complex biological interactions. Network-based approaches have been highly effective in predicting node labels and uncovering hidden associations between entities. These methods have been widely applied in areas such as user-item recommendations, gene-gene interactions, and lncRNA-disease association prediction. In this study, we present GMFLDA, an advanced machine learning framework for inferring lncRNA-disease associations (LDA) by synergizing graph convolutional networks (GCNs) with deep matrix factorization. Specifically, GCNs are leveraged to extract and encode high-fidelity feature representations of lncRNAs and diseases, while deep matrix factorization, implemented via a multi-layer perceptron, facilitates the discovery of potential disease-associated lncRNAs. Our model exhibits outstanding predictive performance, achieving AUCs of 0.9183 and 0.9057 in leave-one-out and five-fold cross-validation experiments, respectively. Extensive comparative evaluations demonstrate that GMFLDA surpasses five state-of-the-art methods, underscoring its superior predictive capability. We anticipate that GMFLDA will serve as a powerful computational tool for biomarker discovery, significantly accelerating the identification of disease-associated lncRNAs while mitigating the time and cost constraints of traditional wet-lab experiments.