Identification of human microRNA-disease association via hypergraph embedded bipartite local model |
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| Affiliation: | 1. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China;2. School of Computer Science and Technology, College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;3. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, USA |
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| 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. |
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| Keywords: | Human MicroRNA-disease association Bipartite network Hypergraph learning Laplacian support vector machine Graph regularized model |
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