ArticleMolecular therapy : the journal of the American Society of Gene Therapy2022
Hierarchical graph attention network for miRNA-disease association prediction.
Article in Molecular therapy : the journal of the American Society of Gene Therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 73 citations in OpenAlex.
- Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis.Frontiers in pharmacology · 2024Pooled it
- A dual-view fusion framework using residual gated graph convolution and Mamba for metabolite-disease association prediction with explorations on salivary metabolites.Journal of computer-aided molecular design · 2026Article
- Attention-Guided Multi-View Contrastive Learning for Predicting Sparse Drug-Gene Associations.Interdisciplinary sciences, computational life sciences · 2026Article
- MV-HAGCN: Prediction of miRNA-Disease Association Based on Multi-View Hybrid Attention Graph Convolutional Network.International journal of molecular sciences · 2026Article
- AMFCL: Predicting miRNA-Disease Associations Through Adaptive Multi-source Modality Fusion and Contrastive Learning.Interdisciplinary sciences, computational life sciences · 2026Article
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- miCDER: a context-aware transformer model for joint miRNA-disease entity and multi-level regulatory relation extraction.BMC genomics · 2025Article
- Graph attention networks-based prediction of MicroRNA-disease causality in head and neck neoplasms.Scientific reports · 2025Article
- Ensemble learning methods and heterogeneous graph network fusion: building drug-gene-disease triple association prediction models.Briefings in bioinformatics · 2025Article
- MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.Briefings in bioinformatics · 2025Article
- Article
- Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Prediction of miRNA-disease associations based on PCA and cascade forest.BMC bioinformatics · 2024Article
- Predicting Disease-Metabolite Associations Based on the Metapath Aggregation of Tripartite Heterogeneous Networks.Interdisciplinary sciences, computational life sciences · 2024Article
- DGNMDA: Dual Heterogeneous Graph Neural Network Encoder for miRNA-Disease Association Prediction.Bioengineering (Basel, Switzerland) · 2024Article
- Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.Briefings in bioinformatics · 2024Article
- MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA-disease associations prediction.Briefings in bioinformatics · 2024Article
- GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.Scientific reports · 2024Article
- HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.Non-coding RNA · 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
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many biological studies show that the mutation and abnormal expression of microRNAs (miRNAs) could cause a variety of diseases. As an important biomarker for disease diagnosis, miRNA is helpful to understand pathogenesis, and could promote the identification, diagnosis and treatment of diseases. However, the pathogenic mechanism how miRNAs affect these diseases has not been fully understood. Therefore, predicting the potential miRNA-disease associations is of great importance for the development of clinical medicine and drug research. In this study, we proposed a novel deep learning model based on hierarchical graph attention network for predicting miRNA-disease associations (HGANMDA). Firstly, we constructed a miRNA-disease-lncRNA heterogeneous graph based on known miRNA-disease associations, miRNA-lncRNA associations and disease-lncRNA associations. Secondly, the node-layer attention was applied to learn the importance of neighbor nodes based on different meta-paths. Thirdly, the semantic-layer attention was applied to learn the importance of different meta-paths. Finally, a bilinear decoder was employed to reconstruct the connections between miRNAs and diseases. The extensive experimental results indicated that our model achieved good performance and satisfactory results in predicting miRNA-disease associations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.