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.
journal_name
Comput Biol Chemjournal_title
Computational biology and chemistryauthors
Ding Y,Jiang L,Tang J,Guo Fdoi
10.1016/j.compbiolchem.2020.107369subject
Has Abstractpub_date
2020-12-01 00:00:00pages
107369eissn
1476-9271issn
1476-928Xpii
S1476-9271(20)30933-6journal_volume
89pub_type
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