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Community Detection in Protein-Protein Interaction Networks Using Spectral and Graph Approaches

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2013)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 8452))

Abstract

Inferring significant communities of interacting proteins is a main trend of current biological research, as this task can help in revealing the functionality and the relevance of specific macromolecular assemblies or even in discovering possible proteins affecting a specific biological process. Efficient algorithms able to find suitable communities inside proteins networks may support drug discovery and diseases treatment even in earlier stages. This paper employs spectral and graph clustering methodologies for discovering protein-protein interactions communities in the Saccharomyces cerevisiae protein-protein interaction network.

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Acknowledgements

Work partially funded by a grant of the University of Genova. Hassan Mahmoud is a PhD student in Computer Science at DIBRIS, University of Genova.

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Authors and Affiliations

  1. DIBRIS - Dipartimento di Informatica, Bioingegneria, Robotica e Ingegneria dei Sistemi, Università di Genova, Via Dodecaneso 35, 16146, Genova, Italy

    Hassan Mahmoud, Francesco Masulli & Stefano Rovetta

  2. Sbarro Institute for Cancer Research and Molecular Medicine, College of Science and Technology, Temple University, Philadelphia, PA, USA

    Francesco Masulli & Giuseppe Russo

Authors
  1. Hassan Mahmoud
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  2. Francesco Masulli
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  3. Stefano Rovetta
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  4. Giuseppe Russo
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Corresponding author

Correspondence to Hassan Mahmoud .

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Editors and Affiliations

  1. University Nice Sophia Antipolis, Sophia Antipolis, France

    Enrico Formenti

  2. University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

  3. University of Groningen, AG Groningen, The Netherlands

    Ernst Wit

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Mahmoud, H., Masulli, F., Rovetta, S., Russo, G. (2014). Community Detection in Protein-Protein Interaction Networks Using Spectral and Graph Approaches. In: Formenti, E., Tagliaferri, R., Wit, E. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2013. Lecture Notes in Computer Science(), vol 8452. Springer, Cham. https://doi.org/10.1007/978-3-319-09042-9_5

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  • DOI: https://doi.org/10.1007/978-3-319-09042-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09041-2

  • Online ISBN: 978-3-319-09042-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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