PmDNE: Prediction of miRNA-Disease Association Based on Network Embedding and Network Similarity Analysis.

Content Provider	Europe PMC
Author	Li, Junyi Liu, Ying Zhang, Zhongqing Liu, Bo Wang, Yadong
Copyright Year	2020
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.
ISSN	23146133
Journal	Biomed Research International
Volume Number	2020
PubMed Central reference number	PMC7735824
PubMed reference number	33354569
e-ISSN	23146141
DOI	10.1155/2020/6248686
Language	English
Publisher	Hindawi
Publisher Date	2020-12-07
Access Restriction	Open
Rights License	This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Copyright © 2020 Junyi Li et al.
Content Type	Text
Resource Type	Article
Subject	Immunology and Microbiology Medicine Biochemistry, Genetics and Molecular Biology