Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 1
163-169

Essential protein identification algorithm based on weighted subnetwork participation degree and multi-source information fusion

Fei Zhaojie1a,2
Liu Peiqiang1a,2
Guo Junhong1b
Yang Zhuang1a,2
Liu Chang1a,2
1. a. School of Computer Science & Technology, b. School of Statistics, Shandong Technology & Business University, Yantai Shandong 264005, China
2. Future Intelligent Computing Co-Innovation Center of Shandong Colleges & Universities, Yantai Shandong 264005, China
DOI: 10.19734/j.issn.1001-3695.2021.04.0161

Abstract

Existing essential protein recognition algorithms don't consider biological information comprehensively, and the recognition accuracy rate needs to be improved. To solve this problem, this paper proposed an efficient essential protein identification algorithm named PDWS. First, it combined the protein position obtained from the subcellular localization information and the edge clustering coefficient of the protein interaction network to construct a weighted network. Second, based on the analysis of the subcellular location of the protein, it proposed a subcellular location compartment subnetwork participation index. Finally, integrating subcellular localization compartment subnetwork participation index and protein complex subnetwork participation index, it multi-dimensionally measured the criticality of protein. The experimental results on the two standard datasets of DIP and Krogan show that PDWS can identify more specific structured essential proteins with recognition accuracies reaching 0.76 and 0.73 respectively, which shows PDWS outperforms PeC, PCSD and other existing algorithms.

Foundation Support

山东省研究生教育质量提升计划资助项目(SDYKC19199)
山东省自然科学基金资助项目(ZR2017MF049)
烟台市重点研发计划资助项目(2017ZH065)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.04.0161
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 1
Section: Algorithm Research & Explore
Pages: 163-169
Serial Number: 1001-3695(2022)01-029-0163-07

Publish History

[2022-01-05] Printed Article

Cite This Article

费兆杰, 刘培强, 郭俊宏, 等. 基于加权子网参与度和多源信息融合的关键蛋白质识别算法 [J]. 计算机应用研究, 2022, 39 (1): 163-169. (Fei Zhaojie, Liu Peiqiang, Guo Junhong, et al. Essential protein identification algorithm based on weighted subnetwork participation degree and multi-source information fusion [J]. Application Research of Computers, 2022, 39 (1): 163-169. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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