Identification of human microRNA-disease association via hypergraph embedded bipartite local model

Yijie Ding; Limin Jiang; Jijun Tang; Fei Guo

doi:10.1016/j.compbiolchem.2020.107369

Identification of human microRNA-disease association via hypergraph embedded bipartite local model

Comput Biol Chem. 2020 Dec:89:107369. doi: 10.1016/j.compbiolchem.2020.107369. Epub 2020 Sep 14.

Authors

Yijie Ding¹, Limin Jiang², Jijun Tang³, Fei Guo⁴

Affiliations

¹ School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China. Electronic address: wuxi_dyj@163.com.
² School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China.
³ School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China; Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA.
⁴ School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China. Electronic address: fguo@tju.edu.cn.

PMID: 33099120
DOI: 10.1016/j.compbiolchem.2020.107369

Abstract

MicroRNA (miRNA) plays an important role in life processes. In recent years, predicting the association between miRNAs and diseases has become a research hotspot. However, biological experiments take a lot of time and cost to identify pathogenic miRNAs. Computational biology-based methods can effectively improve accuracy of recognition. In our study, miRNAs-disease associations are predicted by a hypergraph regularized bipartite local model (HGBLM), which is based on hypergraph embedded Laplacian support vector machine (LapSVM). On benchmark dataset, the results of our method are comparable and even better than existing models.

Keywords: Bipartite network; Graph regularized model; Human MicroRNA-disease association; Hypergraph learning; Laplacian support vector machine.

MeSH terms

Algorithms
Computational Biology / methods*
Genetic Predisposition to Disease*
Humans
MicroRNAs / genetics*
Models, Biological*
Support Vector Machine

Substances

MicroRNAs