ArticleBioMed research international2020
PmDNE: Prediction of miRNA-Disease Association Based on Network Embedding and Network Similarity Analysis.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Graph embedding on mass spectrometry- and sequencing-based biomedical data.BMC bioinformatics · 2024Review
- Diagnostic Value of Coronary Computed Tomography Angiography Image under Automatic Segmentation Algorithm for Restenosis after Coronary Stenting.Contrast media & molecular imaging · 2022Article
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Authors and funding
5 authors.
Funding
Abstract
Successful prediction of miRNA-disease association is nontrivial for the diagnosis and prognosis of genetic diseases. There are many methods to predict miRNA and disease, but biological data are numerous and complex, and they often exist in the form of network. How to accurately use the features of miRNA and disease-related biological networks to predict unknown association has always been a challenge. Here, we propose PmDNE, a method based on network embedding and network similarity analysis, to predict the miRNA-disease association. In PmDNE, the structure of network bipartite graph is improved, and a random walk generator is designed. For embedded vectors, 128 dimensions are used, and the accuracy of prediction is significantly improved. Compared with other network embedding methods, PmDNE is comparable and competitive with the state of art methods. Our method can solve the problem of feature extraction, reduce the dimension of features, and improve the efficiency of miRNA-disease association prediction. This method can also be extended to other area for biomedical network prediction.
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Registered trials
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