ArticleScientific reports2020
Predicting miRNA-based disease-disease relationships through network diffusion on multi-omics biological data.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Non-coding RNAs in exercise immunology: A systematic review.Journal of sport and health science · 2024Pooled it
- Non-coding RNAs: the architects of placental development and pregnancy success.Molecular genetics and genomics : MGG · 2025Review
- Serum and Plasma miRNA Expression Levels in Sudden Sensorineural Hearing Loss.International journal of molecular sciences · 2025Article
- Modular networks and genomic variation during progression from stable angina pectoris through ischemic cardiomyopathy to chronic heart failure.Molecular medicine (Cambridge, Mass.) · 2022Article
- Review
- Article
- Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph.BMC medical genomics · 2021Article
- Risk Prediction of Cardiovascular Events by Exploration of Molecular Data with Explainable Artificial Intelligence.International journal of molecular sciences · 2021Review
- Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering.BMC medical informatics and decision making · 2021Article
- Role of Exosomal MicroRNAs and Their Crosstalk with Oxidative Stress in the Pathogenesis of Osteoporosis.Oxidative medicine and cellular longevity · 2021Review
- Path-based extensions of local link prediction methods for complex networks.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With critical roles in regulating gene expression, miRNAs are strongly implicated in the pathophysiology of many complex diseases. Experimental methods to determine disease related miRNAs are time consuming and costly. Computationally predicting miRNA-disease associations has potential applications in finding miRNA therapeutic pathways and in understanding the role of miRNAs in disease-disease relationships. In this study, we propose the MiRNA-disease Association Prediction (MAP) method, an in-silico method to predict and prioritize miRNA-disease associations. The MAP method applies a network diffusion approach, starting from the known disease genes in a heterogenous network constructed from miRNA-gene associations, protein-protein interactions, and gene-disease associations. Validation using experimental data on miRNA-disease associations demonstrated superior performance to two current state-of-the-art methods, with areas under the ROC curve all over 0.8 for four types of cancer. MAP is successfully applied to predict differential miRNA expression in four cancer types. Most strikingly, disease-disease relationships in terms of shared miRNAs revealed hidden disease subtyping comparable to that of previous work on shared genes between diseases, with applications for multi-omics characterization of disease relationships.
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Registered trials
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