IR
A mixed clustering coefficient centrality for identifying essential proteins
Lu, Pengli; Yu, JingJuan
2020-04-20
Source PublicationINTERNATIONAL JOURNAL OF MODERN PHYSICS B
ISSN0217-9792
Volume34Issue:10
AbstractEssential 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.
KeywordProtein interaction network essential protein protein complex assessment method
DOI10.1142/S0217979220500903
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[11361033] ; Natural Science Foundation of Gansu Province[1212RJZA029]
WOS Research AreaPhysics
WOS SubjectPhysics, Applied ; Physics, Condensed Matter ; Physics, Mathematical
WOS IDWOS:000531579300005
PublisherWORLD SCIENTIFIC PUBL CO PTE LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.lut.edu.cn/handle/2XXMBERH/64360
Collection兰州理工大学
Corresponding AuthorLu, Pengli
AffiliationLanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China
First Author AffilicationLanzhou University of Technology
Corresponding Author AffilicationLanzhou University of Technology
First Signature AffilicationLanzhou University of Technology
Recommended Citation
GB/T 7714
Lu, Pengli,Yu, JingJuan. A mixed clustering coefficient centrality for identifying essential proteins[J]. INTERNATIONAL JOURNAL OF MODERN PHYSICS B,2020,34(10).
APA Lu, Pengli,&Yu, JingJuan.(2020).A mixed clustering coefficient centrality for identifying essential proteins.INTERNATIONAL JOURNAL OF MODERN PHYSICS B,34(10).
MLA Lu, Pengli,et al."A mixed clustering coefficient centrality for identifying essential proteins".INTERNATIONAL JOURNAL OF MODERN PHYSICS B 34.10(2020).
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