期刊
COMPUTATIONAL BIOLOGY AND CHEMISTRY
卷 89, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.compbiolchem.2020.107369
关键词
Human MicroRNA-disease association; Bipartite network; Hypergraph learning; Laplacian support vector machine; Graph regularized model
资金
- National Science Foundation of China [NSFC 61772362,61902271 and61972280]
- Natural Science Research of Jiangsu Higher Education Institutions of China [19KJB520014]
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.
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