ArticleBriefings in bioinformatics2023
MHCLMDA: multihypergraph contrastive learning for miRNA-disease association prediction.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- HMHLVI: Hybrid Multi-view Hypergraph Learning with Variational Inference for snoRNA-Drug Association Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Integrating lncRNA data for prediction of miRNA-disease association using network fusion and matrix completion.Journal of computer-aided molecular design · 2026Article
- A model of multi-view contrastive hypergraph learning for predicting circRNA-disease associations.Scientific reports · 2026Article
- MV-HAGCN: Prediction of miRNA-Disease Association Based on Multi-View Hybrid Attention Graph Convolutional Network.International journal of molecular sciences · 2026Article
- EMGMDA: a multi-modal graph neural framework for robust prediction of miRNA-disease associations.BMC genomics · 2026Article
- MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction.Briefings in bioinformatics · 2026Article
- Root-associated protein prediction using a protein large language model and hypergraph convolutional networks.Scientific reports · 2026Article
- From biogenesis to deep modeling: a holistic review of miRNA-disease prediction computational methods with experimental comparison.Briefings in bioinformatics · 2026Review
- An ensemble strategy for piRNA identification through hybrid moment-based feature modeling.Scientific reports · 2025Article
- Identifying potential miRNA-disease associations through an accurate matrix completion approach.Briefings in bioinformatics · 2025Article
- MethPriorGCN: a deep learning tool for inferring DNA methylation prior knowledge and guiding personalized medicine.Briefings in bioinformatics · 2025Article
- Ensemble learning based on matrix completion improves microbe-disease association prediction.Briefings in bioinformatics · 2025Article
- DeepWalk-Based Graph Embeddings for miRNA-Disease Association Prediction Using Deep Neural Network.Biomedicines · 2025Article
- MicroRNA Gets a Mighty Award.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association.Biomedicines · 2025Article
- Incorporating graph representation and mutual attention mechanism for MiRNA-MRNA interaction prediction.Frontiers in genetics · 2025Article
- PSO-FeatureFusion: a general framework for fusing heterogeneous features via particle swarm optimization.Bioinformatics advances · 2025Article
- Type I Diabetes Mellitus impairs cytotoxic immunity through CEACAM5 upregulation in colorectal cancer : Exploring the intersection of autoimmune dysfunction and cancer progression: the role of NF-κB p65 in colorectal cancer.Journal of molecular histology · 2024Article
- ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs.Briefings in bioinformatics · 2024Article
- Predicting disease-associated microbes based on similarity fusion and deep learning.Briefings in bioinformatics · 2024Article
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4 authors.
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Abstract
The correct prediction of disease-associated miRNAs plays an essential role in disease prevention and treatment. Current computational methods to predict disease-associated miRNAs construct different miRNA views and disease views based on various miRNA properties and disease properties and then integrate the multiviews to predict the relationship between miRNAs and diseases. However, most existing methods ignore the information interaction among the views and the consistency of miRNA features (disease features) across multiple views. This study proposes a computational method based on multiple hypergraph contrastive learning (MHCLMDA) to predict miRNA-disease associations. MHCLMDA first constructs multiple miRNA hypergraphs and disease hypergraphs based on various miRNA similarities and disease similarities and performs hypergraph convolution on each hypergraph to capture higher order interactions between nodes, followed by hypergraph contrastive learning to learn the consistent miRNA feature representation and disease feature representation under different views. Then, a variational auto-encoder is employed to extract the miRNA and disease features in known miRNA-disease association relationships. Finally, MHCLMDA fuses the miRNA and disease features from different views to predict miRNA-disease associations. The parameters of the model are optimized in an end-to-end way. We applied MHCLMDA to the prediction of human miRNA-disease association. The experimental results show that our method performs better than several other state-of-the-art methods in terms of the area under the receiver operating characteristic curve and the area under the precision-recall curve.
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