A new method for identifying essential proteins based on edge clustering coefficient

  • Authors:

  • Huan Wang;Min Li;Jianxin Wang;Yi Pan

  • Affiliations:

  • School of Information Science and Engineering, Central South University, Changsha, P.R. China;School of Information Science and Engineering, Central South University, Changsha, P.R. China and Department of Computer Science, Georgia State University, Atlanta, GA;School of Information Science and Engineering, Central South University, Changsha, P.R. China;School of Information Science and Engineering, Central South University, Changsha, P.R. China and Department of Computer Science, Georgia State University, Atlanta, GA

  • Venue:
  • ISBRA'11 Proceedings of the 7th international conference on Bioinformatics research and applications
  • Year:
  • 2011

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Abstract

Identification of essential proteins is key to understanding the minimal requirements for cellular life and important for drug design. Rapid increasing of available protein-protein interaction data has made it possible to detect protein essentiality on network level. A series of centrality measures have been proposed to discover essential proteins based on network topology. However, most of them tended to focus only on topologies of single proteins, but ignored the relevance between interactions and protein essentiality. In this paper, a new method for identifying essential proteins based on edge clustering coefficient, named as SoECC, is proposed. This method binds characteristics of edges and nodes effectively. The experimental results on yeast protein interaction network show that the number of essential proteins discovered by SoECC universally exceeds that discovered by other six centrality measures. Especially, compared to BC and CC, SoECC is 20% higher in prediction accuracy. Moreover, the essential proteins discovered by SoECC show significant cluster effect.