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A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes

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  • Chao Qin
  • Yongqi Sun
  • Yadong Dong

Abstract

Essential proteins are indispensable to the viability and reproduction of an organism. The identification of essential proteins is necessary not only for understanding the molecular mechanisms of cellular life but also for disease diagnosis, medical treatments and drug design. Many computational methods have been proposed for discovering essential proteins, but the precision of the prediction of essential proteins remains to be improved. In this paper, we propose a new method, LBCC, which is based on the combination of local density, betweenness centrality (BC) and in-degree centrality of complex (IDC). First, we introduce the common centrality measures; second, we propose the densities Den1(v) and Den2(v) of a node v to describe its local properties in the network; and finally, the combined strategy of Den1, Den2, BC and IDC is developed to improve the prediction precision. The experimental results demonstrate that LBCC outperforms traditional topological measures for predicting essential proteins, including degree centrality (DC), BC, subgraph centrality (SC), eigenvector centrality (EC), network centrality (NC), and the local average connectivity-based method (LAC). LBCC also improves the prediction precision by approximately 10 percent on the YMIPS and YMBD datasets compared to the most recently developed method, LIDC.

Suggested Citation

  • Chao Qin & Yongqi Sun & Yadong Dong, 2016. "A New Method for Identifying Essential Proteins Based on Network Topology Properties and Protein Complexes," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-30, August.
    • Handle: RePEc:plo:pone00:0161042
    DOI: 10.1371/journal.pone.0161042
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    References listed on IDEAS

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    1. Csaba Pál & Balázs Papp & Laurence D. Hurst, 2003. "Rate of evolution and gene dispensability," Nature, Nature, vol. 421(6922), pages 496-497, January.
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    Cited by:

    1. Dongli Xie & Jianchen Hu & Tong Wu & Kangli Cao & Xiaogang Luo, 2022. "Potential Biomarkers and Drugs for Nanoparticle-Induced Cytotoxicity in the Retina: Based on Regulation of Inflammatory and Apoptotic Genes," IJERPH, MDPI, vol. 19(9), pages 1-25, May.
    2. Chao Qin & Yongqi Sun & Yadong Dong, 2017. "A new computational strategy for identifying essential proteins based on network topological properties and biological information," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.

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