Small nucleolar RNAs (snoRNAs) are increasingly recognized for their involvement in human diseases. Accurate and robust prediction of disease-associated snoRNAs is crucial for accelerating drug discovery and disease treatment. However, the limited availability of biomedical data in this domain poses a significant challenge to the generalization ability of machine learning models. While existing computational methods have made progress, their performance is often constrained by data scarcity. To overcome these limitations, we introduce SPGA, a novel graph representation learning framework designed to enhance snoRNA-disease association prediction. SPGA leverages intrinsic structural features of snoRNAs and diseases to construct interaction-aware graph representations. It then employs residual graph convolutional networks augmented with a hierarchical attention mechanism to learn robust node embeddings for association predictions. Comprehensive experiments demonstrate that SPGA effectively alleviates the data scarcity problem for model generalization and significantly improves state-of-the-art methods in terms of prediction accuracy, achieving an AUC of 0.9812 and an AUPR of 0.9749 under five-fold cross-validation setting. Case studies further validate its efficacy in identifying novel snoRNA-disease associations. Our study provides a potent computational tool for prioritizing candidate disease-related snoRNAs, thereby facilitating the discovery of potential diagnostic biomarkers and therapeutic targets.
Chen et al. (Wed,) studied this question.