ArticleRNA (New York, N.Y.)2022
iSnoDi-LSGT: identifying snoRNA-disease associations based on local similarity constraints and global topological constraints.
Article in RNA (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- From Cell-Free Transcriptomes to Single-Cell Landscapes: Biomarker Discovery and Originating Cell Alteration Analysis via Graph Matrix Factorization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- GCNMF-SDA: predicting snoRNA-disease associations based on graph convolution and non-negative matrix factorization.Briefings in bioinformatics · 2025Article
- Unlocking the life code: a review of SnoRNA functional diversity and disease relevance.Cell communication and signaling : CCS · 2025Review
- CGSDA: inferring snoRNA-disease associations via ChebNetII and GatedGCN.Frontiers in genetics · 2025Article
- GL4SDA: Predicting snoRNA-disease associations using GNNs and LLM embeddings.Computational and structural biotechnology journal · 2025Article
- DiSMVC: a multi-view graph collaborative learning framework for measuring disease similarity.Bioinformatics (Oxford, England) · 2024Article
- SAGESDA: Multi-GraphSAGE networks for predicting SnoRNA-disease associations.Current research in structural biology · 2024Article
- Graph Convolutional Network and Contrastive Learning Small Nucleolar RNA (snoRNA) Disease Associations (GCLSDA): Predicting snoRNA-Disease Associations via Graph Convolutional Network and Contrastive Learning.International journal of molecular sciences · 2023Article
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2 authors.
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Abstract
Growing evidence proves that small nucleolar RNAs (snoRNAs) have important functions in various biological processes, the malfunction of which leads to the emergence and development of complex diseases. However, identifying snoRNA-disease associations is an ongoing challenging task due to the considerable time- and money-consuming biological experiments. Therefore, it is urgent to design efficient and economical methods for the identification of snoRNA-disease associations. In this regard, we propose a computational method named iSnoDi-LSGT, which utilizes snoRNA sequence similarity and disease similarity as local similarity constraints. The iSnoDi-LSGT predictor further employs network embedding technology to extract topological features of snoRNAs and diseases, based on which snoRNA topological similarity and disease topological similarity are calculated as global topological constraints. To the best of our knowledge, the iSnoDi-LSGT is the first computational method for snoRNA-disease association identification. The experimental results indicate that the iSnoDi-LSGT predictor can effectively predict unknown snoRNA-disease associations. The web server of the iSnoDi-LSGT predictor is freely available at http://bliulab.net/iSnoDi-LSGT.
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