A Novel Method to Predict Essential Proteins Based on Diffusion Distance Networks
| Autor: | Zhihong Zhang, Bihai Zhao, Xiao Han, Lei Wang, Xiner Liu, Sai Hu, Yingchun Luo |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Modularity (networks) diffusion distance General Computer Science Computer science 0206 medical engineering General Engineering 02 engineering and technology Computational biology Construct (python library) Modularity Protein–protein interaction protein-protein interaction 03 medical and health sciences 030104 developmental biology Feature (machine learning) General Materials Science Essential proteins lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:TK1-9971 020602 bioinformatics |
| Zdroj: | IEEE Access, Vol 8, Pp 29385-29394 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Essential proteins are important for the survival and reproduction of organisms. Many computational methods have been proposed to identify essential proteins, due to the production of vast amounts of protein-protein interaction (PPI) data. It has been demonstrated that PPI networks have graph-theoretic characteristics as so-called small-world and scale-free. The traditional metrics cannot really reflect the relationship between proteins when identifying essential proteins from PPI networks. In this paper, we construct a diffusion distance network (DSN) by combining PPI topology characteristics with orthologous proteins and sub-cellular localization information of proteins. Taking the modularity feature of essential proteins into account, we proposed a new essential proteins prediction method based on DSN. We employed our DSN method and ten other state-of-the-art methods to predict essential proteins. The precision-recall curve, jackknife methodology and so on are used to test the performance of these methods. Experimental results show that our method outperform ten other competitive methods. The row data and the software are freely available at: https://github.com/husaiccsu/DSN . |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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