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Essential Proteins Discovery from Weighted Protein Interaction Networks

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Bioinformatics Research and Applications (ISBRA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 6053))

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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.

This work is supported in part by the National Natural Science Foundation of China under Grant No.60773111, the Ph.D. Programs Foundation of Ministry of Education of China No. 20090162120073, the U.S. National Science Foundation under Grants CCF-0514750, CCF-0646102, and CNS-0831634, and the Program for Changjiang Scholars and Innovative Research Team in University No. IRT0661.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Central South University, Changsha, 410083, P.R. China

    Min Li, Jianxin Wang, Huan Wang & Yi Pan

  2. Department of Computer Science, Georgia State University, Atlanta, GA, 30302-4110, USA

    Jianxin Wang & Yi Pan

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  1. Min Li
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  2. Jianxin Wang
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  3. Huan Wang
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  4. Yi Pan
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Editor information

Editors and Affiliations

  1. School of Biology, Department of Biomedical Engineering and College of Computing, Georgia Institute of Technology, Atlanta, Georgia, USA

    Mark Borodovsky

  2. Molecular and Cell Biology Department, University of Connecticut, 91 North Eagleville Road, Unit 3125, 06269-3125, Storrs, CT, USA

    Johann Peter Gogarten

  3. National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Building 38A 8S814, 20894, Bethesda, MD, USA

    Teresa M. Przytycka

  4. Department of Computer Science and Engineering, University of Connecticut, 257 ITE Building, 371 Fairfield Way, 06269-2155, Storrs, CT, USA

    Sanguthevar Rajasekaran

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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. https://doi.org/10.1007/978-3-642-13078-6_11

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  • DOI: https://doi.org/10.1007/978-3-642-13078-6_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13077-9

  • Online ISBN: 978-3-642-13078-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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