Graph Convolutional Network and Contrastive Learning Small Nucleolar RNA (snoRNA) Disease Associations (GCLSDA): Predicting snoRNA-Disease Associations via Graph Convolutional Network and Contrastive Learning.Small nucleolar RNAs (snoRNAs) constitute a prevalent class of noncoding RNAs localized within the nucleoli of eukaryotic cells.  Herein, we introduce GCLSDA-graph Convolutional Network and contrastive learning predict snoRNA disease associations.  GCLSDA is an innovative framework that combines graph convolution networks and self-supervised learning for snoRNA-disease association prediction.  With careful consideration, GCLSDA intelligently incorporates contrast learning to address the challenging issues of sparsity and over-smoothing inside correlation matrices.  In the area of snoRNA-disease associations, GCLSDA constantly shows its promising capacity for prediction through extensive research. 
GCLSDA
Graph Convolutional Network and Contrastive Learning Small Nucleolar RNA (snoRNA) Disease Associations (GCLSDA): Predicting snoRNA-Disease Associations via Graph Convolutional Network and Contrastive Learning.Small nucleolar RNAs (snoRNAs) constitute a prevalent class of noncoding RNAs localized within the nucleoli of eukaryotic cells. 
Graph Convolutional Network and Contrastive Learning Small Nucleolar RNA (snoRNA) Disease Associations
Leveraging the repository of MNDR v4.0 and ncRPheno databases, we construct a robust snoRNA-disease association dataset, which serves as the foundation to create bipartite graphs. 
MNDR v4.0
Leveraging the repository of MNDR v4.0 and ncRPheno databases, we construct a robust snoRNA-disease association dataset, which serves as the foundation to create bipartite graphs. 
ncRPheno databases