HMHLVI: Hybrid Multi-view Hypergraph Learning with Variational Inference for snoRNA-Drug Association Prediction

Traditionally recognized for guiding rRNA modifications, small nucleolar RNAs (snoRNAs) are increasingly appreciated as key regulators of drug response. However, snoRNA and drug association data remain limited, and computational approaches capable of capturing their complex, high-order relationships are scarce. Here, we present HMHLVI, a hybrid multi-view hypergraph learning with variational inference framework that integrates sequence, structural, and association information to predict snoRNA-drug response associations. HMHLVI constructs three higher-order networks called attribute, topology, and association to model intrinsic features, structural dependencies, and known interactions, respectively. By employing shared and modality-specific hypergraph convolutional encoders together with an adaptive temperature-regulated multi-view attention mechanism, the framework effectively learns both common and view-specific biomolecular representations. In addition, a variational autoencoder is introduced to model the higher-order association network and to capture latent interaction patterns in a low-dimensional probabilistic space, enhancing robustness and denoising capability. Across three cross-validation settings, including random zero, multi-column zero, and multi-row zero, HMHLVI consistently outperformed other state-of-the-art models. System-level validations including functional enrichment, molecular docking, thermodynamic analysis, and clinical survival assessment confirmed its biological relevance. Key regulatory snoRNAs such as SNORD43 and SNORD116 were identified, and an integrated network linking drugs, diseases, snoRNAs, and target genes was constructed. Notably, SCARNA6 was predicted to modulate docetaxel response in esophageal squamous cell carcinoma, and higher SCARNA6 expression was associated with favorable overall survival in an exploratory Kaplan-Meier analysis (log-rank p = 0.0029).