Essential Proteins Discovery from Weighted Protein Interaction Networks

  • Min Li
  • Jianxin Wang
  • Huan Wang
  • Yi Pan
Conference paper

DOI: 10.1007/978-3-642-13078-6_11

Volume 6053 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Li M., Wang J., Wang H., Pan Y. (2010) Essential Proteins Discovery from Weighted Protein Interaction Networks. In: Borodovsky M., Gogarten J.P., Przytycka T.M., Rajasekaran S. (eds) Bioinformatics Research and Applications. ISBRA 2010. Lecture Notes in Computer Science, vol 6053. Springer, Berlin, Heidelberg

Abstract

Identifying essential proteins is important for understanding the minimal requirements for cellular survival and development. Fast growth in the amount of available protein-protein interactions has produced unprecedented opportunities for detecting 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 treat all interactions equally and are sensitive to false positives. In this paper, six standard centrality measures are redefined to be used in weighted network. A new method for weighing protein-protein interactions is proposed based on the combination of logistic regression-based model and function similarity. The experimental results on yeast network show that the weighting method can improve the performance of centrality measures considerably. More essential proteins are discovered by the weighted centrality measures than by the original centrality measures used in unweighted network. Even about 20% improvements are obtained from closeness centrality and subgraph centrality.

Keywords

essential protein protein interaction network centrality 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Min Li
    • 1
  • Jianxin Wang
    • 1
    • 2
  • Huan Wang
    • 1
  • Yi Pan
    • 1
    • 2
  1. 1.School of Information Science and EngineeringCentral South UniversityChangshaP.R. China
  2. 2.Department of Computer ScienceGeorgia State UniversityAtlantaUSA