GPLMD: A Multi-Task Graph Learning Framework for inferring the relationships among lncRNAs, miRNAs and diseases

Long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and their potential relationships with diseases play a crucial role in disease prevention, diagnosis, and treatment. However, experimental validation is resource-intensive, making computational methods an essential tool for addressing this challenge. Most existing methods focus on single tasks and fail to leverage the shared knowledge across related prediction tasks, while also lacking the ability to model complex biological relationships from diverse graph structures and fine-grained node interactions. To overcome these limitations, we propose a novel model, GPLMD, integrating multiple graph learning and pairwise learning for inferring the relationships among lncRNAs, miRNAs and diseases. First, we construct bipartite graphs, feature structural graphs, and meta-path graphs based on the multiple connections between biomolecules. Next, we employ a graph convolutional network (GCN)-based decoder to learn the multi-type neighbor topology of each node. Additionally, we introduce a further learning approach on different graph views, combining attention mechanisms and semantic fusion to generate richer global representations. Finally, we utilize convolutional neural network (CNN) to learn the relational features between nodes and integrate them with the node embeddings obtained from graph learning, performing joint optimization to complete the classification task. The extensive results from two benchmark datasets clearly demonstrate that GPLMD outperforms other baseline methods in LDA, MDA, and LMI prediction tasks. The case studies further confirm the ability of GPLMD to identify novel disease-related candidate lncRNAs and miRNAs.