A mixed clustering coefficient centrality for identifying essential proteins

doi:10.1142/S0217979220500903

	A mixed clustering coefficient centrality for identifying essential proteins
	Lu, Pengli; Yu, JingJuan
	2020-04-20
发表期刊	INTERNATIONAL JOURNAL OF MODERN PHYSICS B
ISSN	0217-9792
卷号	34 期号:10
摘要	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.
关键词	Protein interaction network essential protein protein complex assessment method
DOI	10.1142/S0217979220500903
收录类别	SCI ; SCIE
语种	英语
资助项目	National Natural Science Foundation of China[11361033] ; Natural Science Foundation of Gansu Province[1212RJZA029]
WOS研究方向	Physics
WOS类目	Physics, Applied ; Physics, Condensed Matter ; Physics, Mathematical
WOS记录号	WOS:000531579300005
出版者	WORLD SCIENTIFIC PUBL CO PTE LTD
来源库	WOS
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.lut.edu.cn/handle/2XXMBERH/64360
专题	兰州理工大学
通讯作者	Lu, Pengli
作者单位	Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China
第一作者单位	兰州理工大学
通讯作者单位	兰州理工大学
第一作者的第一单位	兰州理工大学
推荐引用方式 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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