ArticlePLoS computational biology2023
RMDGCN: Prediction of RNA methylation and disease associations based on graph convolutional network with attention mechanism.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Computational prediction of RNA modification site-disease associations: a systematic review.Briefings in bioinformatics · 2026Pooled it
- A Systematic Review of the Application of Graph Neural Networks to Extract Candidate Genes and Biological Associations.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Pooled it
- mInterdisciplinary sciences, computational life sciences · 2026Article
- THGC_MDA: a method for predicting the associations between mFrontiers in genetics · 2026Article
- Multimodal zero-shot learning of previously unseen epitranscriptomes from RNA-seq data.Briefings in bioinformatics · 2025Article
- DMGAT: predicting ncRNA-drug resistance associations based on diffusion map and heterogeneous graph attention network.Briefings in bioinformatics · 2025Article
- An Integrated TCN-CrossMHA Model for Predicting circRNA-RBP Binding Sites.Interdisciplinary sciences, computational life sciences · 2025Article
- Statistical modeling of single-cell epitranscriptomics enabled trajectory and regulatory inference of RNA methylation.Cell genomics · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA modification is a post transcriptional modification that occurs in all organisms and plays a crucial role in the stages of RNA life, closely related to many life processes. As one of the newly discovered modifications, N1-methyladenosine (m1A) plays an important role in gene expression regulation, closely related to the occurrence and development of diseases. However, due to the low abundance of m1A, verifying the associations between m1As and diseases through wet experiments requires a great quantity of manpower and resources. In this study, we proposed a computational method for predicting the associations of RNA methylation and disease based on graph convolutional network (RMDGCN) with attention mechanism. We build an adjacency matrix through the collected m1As and diseases associations, and use positive-unlabeled learning to increase the number of positive samples. By extracting the features of m1As and diseases, a heterogeneous network is constructed, and a GCN with attention mechanism is adopted to predict the associations between m1As and diseases. The experimental results indicate that under a 5-fold cross validation, RMDGCN is superior to other methods (AUC = 0.9892 and AUPR = 0.8682). In addition, case studies indicate that RMDGCN can predict the relationships between unknown m1As and diseases. In summary, RMDGCN is an effective method for predicting the associations between m1As and diseases.
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