ArticleBioMed research international2015
Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
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Who cites it
94 citing papers in PubMed.
- Unbiased microRNA-Disease Association Prediction Using ICD-11 Codes and Negative Sampling.Pharmacology research & perspectives · 2025Article
- LanDis: the disease landscape explorer.European journal of human genetics : EJHG · 2024Article
- KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection.BMC bioinformatics · 2023Article
- Machine learning for RNA 2D structure prediction benchmarked on experimental data.Briefings in bioinformatics · 2023Review
- DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.Computers & chemical engineering · 2022Article
- 67-kDa Laminin Receptor-Mediated Cellular Sensing System of Green Tea Polyphenol EGCG and Functional Food Pairing.Molecules (Basel, Switzerland) · 2022Review
- Exploring diet associations with Covid-19 and other diseases: a Network Analysis-based approach.Medical & biological engineering & computing · 2022Article
- Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers.Frontiers in bioengineering and biotechnology · 2022Review
- Review of machine learning methods for RNA secondary structure prediction.PLoS computational biology · 2021Review
- Drug repositioning based on similarity constrained probabilistic matrix factorization: COVID-19 as a case study.Applied soft computing · 2021Article
- Analysis on the Spatial-Temporal Evolution Characteristics and Spatial Network Structure of Tourism Eco-Efficiency in the Yangtze River Delta Urban Agglomeration.International journal of environmental research and public health · 2021Article
- Fusion of KATZ measure and space projection to fast probe potential lncRNA-disease associations in bipartite graphs.PloS one · 2021Article
- A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method.PloS one · 2021Article
- NEMPD: a network embedding-based method for predicting miRNA-disease associations by preserving behavior and attribute information.BMC bioinformatics · 2020Article
- Comparative analysis of similarity measurements in miRNAs with applications to miRNA-disease association predictions.BMC bioinformatics · 2020Article
- Graph regularized LBMC bioinformatics · 2020Article
- gutMDisorder: a comprehensive database for dysbiosis of the gut microbiota in disorders and interventions.Nucleic acids research · 2020Article
- Prediction of Potential Associations Between miRNAs and Diseases Based on Matrix Decomposition.Frontiers in genetics · 2020Article
- BHCMDA: A New Biased Heat Conduction Based Method for Potential MiRNA-Disease Association Prediction.Frontiers in genetics · 2020Article
- 6mA-RicePred: A Method for Identifying DNAFrontiers in plant science · 2020Article
34 more citing papers are in PubMed but not listed here.
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.
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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.