ArticleBMC bioinformatics2023
HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Dynamic modifications of circular RNAs drive oncogenesis.Epigenomics · 2025Review
- circGPAcorr: an integrative tool for functional annotation of circular RNAs using expression data.BioData mining · 2025Article
- Cross-attention graph neural networks for inferring gene regulatory networks with skewed degree distribution.BMC bioinformatics · 2025Article
- Circular RNAs modulate cancer drug resistance: advances and challenges.Cancer drug resistance (Alhambra, Calif.) · 2025Article
- Heterogeneous biomedical entity representation learning for gene-disease association prediction.Briefings in bioinformatics · 2024Article
- BPP: a platform for automatic biochemical pathway prediction.Briefings in bioinformatics · 2024Article
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Authors and funding
9 authors.
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Abstract
Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can act as biomarkers of diseases. However, traditional experimental methods for circRNA-disease associations identification are labor-intensive. In this work, we propose a novel method based on the heterogeneous graph neural network and metapaths for circRNA-disease associations prediction termed as HMCDA. First, a heterogeneous graph consisting of circRNA-disease associations, circRNA-miRNA associations, miRNA-disease associations and disease-disease associations are constructed. Then, six metapaths are defined and generated according to the biomedical pathways. Afterwards, the entity content transformation, intra-metapath and inter-metapath aggregation are implemented to learn the embeddings of circRNA and disease entities. Finally, the learned embeddings are used to predict novel circRNA-disase associations. In particular, the result of extensive experiments demonstrates that HMCDA outperforms four state-of-the-art models in fivefold cross validation. In addition, our case study indicates that HMCDA has the ability to identify novel circRNA-disease associations.
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