Lanzhou University of Technology Institutional Repository （LUT_IR）
| A mixed clustering coefficient centrality for identifying essential proteins | |
| Lu, Pengli; Yu, JingJuan | |
| 2020-04-20 | |
| Source Publication | INTERNATIONAL JOURNAL OF MODERN PHYSICS B
 |
| ISSN | 0217-9792 |
| Volume | 34Issue:10 |
| 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. |
| Keyword | Protein interaction network essential protein protein complex assessment method |
| DOI | 10.1142/S0217979220500903 |
| Indexed By | SCI ; SCIE |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[11361033] ; Natural Science Foundation of Gansu Province[1212RJZA029] |
| WOS Research Area | Physics |
| WOS Subject | Physics, Applied ; Physics, Condensed Matter ; Physics, Mathematical |
| WOS ID | WOS:000531579300005 |
| Publisher | WORLD SCIENTIFIC PUBL CO PTE LTD |
| Source library | WOS |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lut.edu.cn/handle/2XXMBERH/64360 |
| Collection | 兰州理工大学 |
| Corresponding Author | Lu, Pengli |
| Affiliation | Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou 730050, Gansu, Peoples R China |
| First Author Affilication | Lanzhou University of Technology |
| Corresponding Author Affilication | Lanzhou University of Technology |
| First Signature Affilication | Lanzhou 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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| File Name/Size | DocType | Version | Access | License | ||
| Lu-2020-A mixed clus（1721KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Download | |
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