ArticleBMC systems biology2013
Walking the interactome to identify human miRNA-disease associations through the functional link between miRNA targets and disease genes.
Article in BMC systems biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 121 papers, 1 of them a synthesis that pooled it.
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Who cites it
121 citing papers in PubMed, 1 synthesis or guideline pooled it, 245 citations in OpenAlex.
- A new method for identifying causal genes of schizophrenia and anti-tuberculosis drug-induced hepatotoxicity.Scientific reports · 2016Pooled it
- MV-HAGCN: Prediction of miRNA-Disease Association Based on Multi-View Hybrid Attention Graph Convolutional Network.International journal of molecular sciences · 2026Article
- Uncovering miRNA-Disease Associations Through Graph Based Neural Network Representations.Biomedicines · 2026Article
- Unbiased microRNA-Disease Association Prediction Using ICD-11 Codes and Negative Sampling.Pharmacology research & perspectives · 2025Article
- DeepWalk-Based Graph Embeddings for miRNA-Disease Association Prediction Using Deep Neural Network.Biomedicines · 2025Article
- Graph Convolutional Network with Neural Collaborative Filtering for Predicting miRNA-Disease Association.Biomedicines · 2025Article
- PanGIA: A universal framework for identifying association between ncRNAs and diseases.GigaScience · 2025Article
- Disentangled similarity graph attention heterogeneous biological memory network for predicting disease-associated miRNAs.BMC genomics · 2024Article
- Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.Briefings in bioinformatics · 2024Article
- MHGTMDA: Molecular heterogeneous graph transformer based on biological entity graph for miRNA-disease associations prediction.Molecular therapy. Nucleic acids · 2024Article
- Three-layer heterogeneous network based on the integration of CircRNA information for MiRNA-disease association prediction.PeerJ. Computer science · 2024Article
- PanomiR: a systems biology framework for analysis of multi-pathway targeting by miRNAs.Briefings in bioinformatics · 2023Article
- Predicting miRNA-disease associations based on PPMI and attention network.BMC bioinformatics · 2023Article
- Combined Treatment of Bronchial Epithelial Calu-3 Cells with Peptide Nucleic Acids Targeting miR-145-5p and miR-101-3p: Synergistic Enhancement of the Expression of the Cystic Fibrosis Transmembrane Conductance Regulator (International journal of molecular sciences · 2022Article
- TLNPMD: Prediction of miRNA-Disease Associations Based on miRNA-Drug-Disease Three-Layer Heterogeneous Network.Molecules (Basel, Switzerland) · 2022Article
- Article
- MDMF: Predicting miRNA-Disease Association Based on Matrix Factorization with Disease Similarity Constraint.Journal of personalized medicine · 2022Article
- Predicting miRNA-disease associations via layer attention graph convolutional network model.BMC medical informatics and decision making · 2022Article
- Application of Bidirectional Generative Adversarial Networks to Predict Potential miRNAs Associated With Diseases.Frontiers in genetics · 2022Article
- A clustering-based sampling method for miRNA-disease association prediction.Frontiers in genetics · 2022Article
61 more citing papers are in PubMed but not listed here.
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases. However, miRNA-disease associations identified by previous computational methods are far from completeness and more effort is needed.
resultsWe developed a computational framework to identify miRNA-disease associations by performing random walk analysis, and focused on the functional link between miRNA targets and disease genes in protein-protein interaction (PPI) networks. Furthermore, a bipartite miRNA-disease network was constructed, from which several miRNA-disease co-regulated modules were identified by hierarchical clustering analysis. Our approach achieved satisfactory performance in identifying known cancer-related miRNAs for nine human cancers with an area under the ROC curve (AUC) ranging from 71.3% to 91.3%. By systematically analyzing the global properties of the miRNA-disease network, we found that only a small number of miRNAs regulated genes involved in various diseases, genes associated with neurological diseases were preferentially regulated by miRNAs and some immunological diseases were associated with several specific miRNAs. We also observed that most diseases in the same co-regulated module tended to belong to the same disease category, indicating that these diseases might share similar miRNA regulatory mechanisms.
conclusionsIn this study, we present a computational framework to identify miRNA-disease associations, and further construct a bipartite miRNA-disease network for systematically analyzing the global properties of miRNA regulation of disease genes. Our findings provide a broad perspective on the relationships between miRNAs and diseases and could potentially aid future research efforts concerning miRNA involvement in disease pathogenesis.
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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.