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

Junyi Li,Zhongqing Zhang,Ying Liu,Yadong Wang,Bo Liu,Zhenguo Zhang

doi:10.1155/2020/6248686

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.

Highlights

MicroRNA is a kind of noncoding RNA with length of around 22 nucleotides
Successful prediction of diseaserelated miRNAs is nontrivial for the diagnosis and prognosis of genetic diseases and drug development
We propose a method based on network embedding and network similarity analysis called PmDNE to predict the miRNA-disease association

Summary

Introduction

MicroRNA (miRNA) is a kind of noncoding RNA with length of around 22 nucleotides. It has been found in plants, animals, and viruses. The second kind of method is based on machine learning This kind of method is able to solve the problem of new miRNAs and disease relation prediction. RLSMDA [14] can calculate miRNA disease association prediction score of new diseases This kind of method needs to solve two major problems: feature extraction and negative case missing. In order to better predict the relationship between disease and miRNA, the network embedding method can be used to solve the problem of feature extraction. 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 be extended to other area for biomedical network prediction

Materials and Methods

Return DR

Results and Discussion

Conclusion

Conflicts of Interest