ArticleBMC medical informatics and decision making2022
Predicting miRNA-disease associations via layer attention graph convolutional network model.
Article in BMC medical informatics and decision making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 10 citations in OpenAlex.
- Computational Method Using Attribute-Aware Message Passing and Graph Convolutional Network for Potential miRNA-Disease Association Prediction.International journal of molecular sciences · 2026Article
- Knowledge-guided graph fusion of mRNA profiles for interpretable cancer subtyping.Briefings in functional genomics · 2026Article
- MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.Briefings in bioinformatics · 2025Article
- Prediction of miRNA-disease associations based on PCA and cascade forest.BMC bioinformatics · 2024Article
- Prediction of miRNA-disease association based on multisource inductive matrix completion.Scientific reports · 2024Article
- ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.Briefings in bioinformatics · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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Abstract
backgroundMiRNA is a class of non-coding single-stranded RNA molecules with a length of approximately 22 nucleotides encoded by endogenous genes, which can regulate the expression of other genes. Therefore, it is very important to predict the associations between miRNA and disease. Predecessors developed a new prediction method of drug-disease association, and it achieved good results.
methodsIn this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. First, we integrate three associations into a heterogeneous network, such as the known miRNA-disease association, miRNA-miRNA similarities and disease-disease similarities, next we apply graph convolution network to learn the embedding of miRNA and disease. We use an attention mechanism to combine embedding from multiple convolution layers. Unobserved miRNA-disease associations are scored based on integrated embedding.
resultsAfter fivefold cross-validations, the value of AUC is reached 0.9091, which is higher than other prediction methods and baseline methods.
conclusionsIn this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. LAGCN has achieved good performance in predicting miRNA-disease associations, and it is superior to other association prediction methods and baseline methods.
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