ArticleJournal of translational medicine2018
A novel semi-supervised model for miRNA-disease association prediction based on
Article in Journal of translational medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- DNI-MDCAP: improvement of causal MiRNA-disease association prediction based on deep network imputation.BMC bioinformatics · 2024Article
- Machine learning in the development of targeting microRNAs in human disease.Frontiers in genetics · 2022Review
- Benchmark of computational methods for predicting microRNA-disease associations.Genome biology · 2019Article
- Self-Weighted Multi-Kernel Multi-Label Learning for Potential miRNA-Disease Association Prediction.Molecular therapy. Nucleic acids · 2019Article
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4 authors.
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Abstract
backgroundIdentification of miRNA-disease associations has attracted much attention recently due to the functional roles of miRNAs implicated in various biological and pathological processes. Great efforts have been made to discover the potential associations between miRNAs and diseases both experimentally and computationally. Although reliable, the experimental methods are in general time-consuming and labor-intensive. In comparison, computational methods are more efficient and applicable to large-scale datasets.
methodsIn this paper, we propose a novel semi-supervised model to predict miRNA-disease associations via
resultsCompared with four state-of-the-art prediction methods, our method achieved favorable performance with AUCs of 0.943 and 0.946 in both global LOOCV and local LOOCV, respectively. In addition, we carried out three types of case studies on five common human diseases, and most of the top 50 predicted miRNAs were confirmed to be associated with the investigated diseases by four databases dbDEMC, PheomiR, miR2Disease and miRwayDB. Specifically, our results provided potential evidence that miRNAs within the same family or cluster were likely to play functional roles together in given diseases.
conclusionsTaken together, the experimental results clearly demonstrated the utility of the proposed method. We anticipated that our method could serve as a reliable and efficient tool for miRNA-disease association prediction.
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