Structure and Semantics Aware Multi-View Contrastive Learning for predicting association among lncRNAs, miRNAs and diseases

Exploring associations among long non coding RNAs (lncRNAs), microRNAs (miRNAs), and dis eases is crucial for biomarker discovery and precision medicine. Existing computational methods are hindered by sparse known associations and the complexity of bi ological networks. To address this challenge, we pro pose SSMVCL (Structure- and Semantic-aware Multi-View Contrastive Learning), a unified framework for predicting lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), and lncRNA-miRNA interactions (LMIs). SSMVCL constructs a heterogeneous bioinformatics network from multi-source biological data and learns representations from two complementary views: a structure-aware view for local topology and a semantic-aware view using biologically meaningful meta-paths to capture high-order relationships. A cross-view contrastive alignment module with adaptive negative sampling enforces consistency between views and enhances discriminative capability. On two benchmark datasets, SSMVCL achieves state-of-the art performance: for Dataset2, AUC/AUPR of 0.9736/0.9716 (LDA), 0.9364/0.9309 (MDA), and 0.9297/0.9234 (LMI) Case studies on gastric and prostate cancers further validated robustness and translational potential by identifying supported associations.