ArticleBiomolecules2022
Predicting miRNA-Disease Association Based on Neural Inductive Matrix Completion with Graph Autoencoders and Self-Attention Mechanism.
Article in Biomolecules, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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23 citing papers in PubMed, 56 citations in OpenAlex.
- Comparative Analysis of Graph Attention and Graph Convolutional Networks for Predicting Candidate SNP-miRNA-DrugInteractions in Integrated Regulatory Datasets.Acta odontologica latinoamericana : AOL · 2026Article
- SGMHA: semantic graph reconstruction with multi-head attention for gene regulatory network inference.BMC genomics · 2026Article
- Prediction of multicategory miRNA-disease associations based on bidirectional hypergraph attention network and gated convolutional strategy.Journal of computer-aided molecular design · 2026Article
- CMV-GLA: contrastive multi-view graph layer attention for predicting phosphorylation site-disease associations.BMC bioinformatics · 2026Article
- A model of multi-view contrastive hypergraph learning for predicting circRNA-disease associations.Scientific reports · 2026Article
- CLAP-HMM: a biologically constrained deep learning framework for resistance gene prediction in long DNA sequences.BMC biotechnology · 2025Article
- Denoising self-supervised learning for disease-gene association prediction.BMC bioinformatics · 2025Article
- Decoding potential lncRNA and disease associations through graph representation learning and gradient boosting with histogram.Scientific reports · 2025Article
- Ethanol Concentration Determination in Baijiu by Graph-Regularized PCA and Random Forest-Based Raman Spectroscopy.ACS omega · 2025Article
- PanGIA: A universal framework for identifying association between ncRNAs and diseases.GigaScience · 2025Article
- Predicting noncoding RNA and disease associations using multigraph contrastive learning.Scientific reports · 2025Article
- OFGPMA: Optimal frequency graph representation learning for pseudogene and miRNA association prediction.Frontiers in genetics · 2025Article
- Personalized movie recommendation in IoT-enhanced systems using graph convolutional network and multi-layer perceptron.Scientific reports · 2024Article
- Synchronous Mutual Learning Network and Asynchronous Multi-Scale Embedding Network for miRNA-Disease Association Prediction.Interdisciplinary sciences, computational life sciences · 2024Article
- SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm.Journal of cellular and molecular medicine · 2024Article
- IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network.Briefings in bioinformatics · 2024Article
- DAEMDA: A Method with Dual-Channel Attention Encoding for miRNA-Disease Association Prediction.Biomolecules · 2023Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- TLCrys: Transfer Learning Based Method for Protein Crystallization Prediction.International journal of molecular sciences · 2022Article
- SVMMDR: Prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many studies have clarified that microRNAs (miRNAs) are associated with many human diseases. Therefore, it is essential to predict potential miRNA-disease associations for disease pathogenesis and treatment. Numerous machine learning and deep learning approaches have been adopted to this problem. In this paper, we propose a Neural Inductive Matrix completion-based method with Graph Autoencoders (GAE) and Self-Attention mechanism for miRNA-disease associations prediction (NIMGSA). Some of the previous works based on matrix completion ignore the importance of label propagation procedure for inferring miRNA-disease associations, while others cannot integrate matrix completion and label propagation effectively. Varying from previous studies, NIMGSA unifies inductive matrix completion and label propagation via neural network architecture, through the collaborative training of two graph autoencoders. This neural inductive matrix completion-based method is also an implementation of self-attention mechanism for miRNA-disease associations prediction. This end-to-end framework can strengthen the robustness and preciseness of both matrix completion and label propagation. Cross validations indicate that NIMGSA outperforms current miRNA-disease prediction methods. Case studies demonstrate that NIMGSA is competent in detecting potential miRNA-disease associations.
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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.