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A mixed clustering coefficient centrality for identifying essential proteins.
- Source :
-
International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics . 4/20/2020, Vol. 34 Issue 10, pN.PAG-N.PAG. 14p. 3 Charts, 8 Graphs. - Publication Year :
- 2020
-
Abstract
- Essential protein plays a crucial role in the process of cell life. The identification of essential proteins not only promotes the development of drug target technology, but also contributes to the mechanism of biological evolution. There are plenty of scholars who pay attention to discover essential proteins according to the topological structure of protein network and biological information. The accuracy of protein recognition still demands to be improved. In this paper, we propose a method which integrates the clustering coefficient in protein complexes and topological properties to determine the essentiality of proteins. First, we give the definition of In-clustering coefficient (IC) to describe the properties of protein complexes. Then we propose a new method, complex edge and node clustering (CENC) coefficient, to identify essential proteins. Different Protein–Protein Interaction (PPI) networks of Saccharomyces cerevisiae, MIPS and DIP are used as experimental materials. Through some experiments of logistic regression model, the results show that the method of CENC can promote the ability of recognizing essential proteins by comparing with the existing methods DC, BC, EC, SC, LAC, NC and the recent UC method. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02179792
- Volume :
- 34
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- International Journal of Modern Physics B: Condensed Matter Physics; Statistical Physics; Applied Physics
- Publication Type :
- Academic Journal
- Accession number :
- 143005610
- Full Text :
- https://doi.org/10.1142/S0217979220500903