IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0072366.html
   My bibliography  Save this article

C-element: A New Clustering Algorithm to Find High Quality Functional Modules in PPI Networks

Author

Listed:
  • Mahdieh Ghasemi
  • Maseud Rahgozar
  • Gholamreza Bidkhori
  • Ali Masoudi-Nejad

Abstract

Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make any difference between a biological network and any other networks. In the current research, we present a new procedure to find functional modules in PPI networks. Our main idea is to model a biological concept and to use this concept for finding good functional modules in PPI networks. In order to evaluate the quality of the obtained clusters, we compared the results of our algorithm with those of some other widely used clustering algorithms on three high throughput PPI networks from Sacchromyces Cerevisiae, Homo sapiens and Caenorhabditis elegans as well as on some tissue specific networks. Gene Ontology (GO) analyses were used to compare the results of different algorithms. Each algorithm's result was then compared with GO-term derived functional modules. We also analyzed the effect of using tissue specific networks on the quality of the obtained clusters. The experimental results indicate that the new algorithm outperforms most of the others, and this improvement is more significant when tissue specific networks are used.

Suggested Citation

  • Mahdieh Ghasemi & Maseud Rahgozar & Gholamreza Bidkhori & Ali Masoudi-Nejad, 2013. "C-element: A New Clustering Algorithm to Find High Quality Functional Modules in PPI Networks," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    • Handle: RePEc:plo:pone00:0072366
    DOI: 10.1371/journal.pone.0072366
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072366
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0072366&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0072366?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Leland H. Hartwell & John J. Hopfield & Stanislas Leibler & Andrew W. Murray, 1999. "From molecular to modular cell biology," Nature, Nature, vol. 402(6761), pages 47-52, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nicola Bellomo & Richard Bingham & Mark A.J. Chaplain & Giovanni Dosi & Guido Forni & Damian A. Knopoff & John Lowengrub & Reidun Twarock & Maria Enrica Virgillito, 2020. "A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world," LEM Papers Series 2020/16, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Lazaros K Gallos & Fabricio Q Potiguar & José S Andrade Jr & Hernan A Makse, 2013. "IMDB Network Revisited: Unveiling Fractal and Modular Properties from a Typical Small-World Network," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-8, June.
    3. T. Ochiai & J. C. Nacher, 2007. "Stochastic analysis of autoregulatory gene expression dynamics," Mathematical and Computer Modelling of Dynamical Systems, Taylor & Francis Journals, vol. 14(4), pages 377-388, November.
    4. Qing-Ju Jiao & Yan-Kai Zhang & Lu-Ning Li & Hong-Bin Shen, 2011. "BinTree Seeking: A Novel Approach to Mine Both Bi-Sparse and Cohesive Modules in Protein Interaction Networks," PLOS ONE, Public Library of Science, vol. 6(11), pages 1-12, November.
    5. Manfred Füllsack, 2011. "Firstness - As seen from the perspective of Complexity Research," E-LOGOS, Prague University of Economics and Business, vol. 2011(1), pages 1-19.
    6. Simeon D. Castle & Michiel Stock & Thomas E. Gorochowski, 2024. "Engineering is evolution: a perspective on design processes to engineer biology," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    7. Romualdo Pastor-Satorras & Eric Smith & Ricard V. Solé, 2002. "Evolving Protein Interaction Networks through Gene Duplication," Working Papers 02-02-008, Santa Fe Institute.
    8. Frederic Li Mow Chee & Bruno Beernaert & Billie G. C. Griffith & Alexander E. P. Loftus & Yatendra Kumar & Jimi C. Wills & Martin Lee & Jessica Valli & Ann P. Wheeler & J. Douglas Armstrong & Maddy Pa, 2023. "Mena regulates nesprin-2 to control actin–nuclear lamina associations, trans-nuclear membrane signalling and gene expression," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    9. Joshua S Weitz & Philip N Benfey & Ned S Wingreen, 2007. "Evolution, Interactions, and Biological Networks," PLOS Biology, Public Library of Science, vol. 5(1), pages 1-3, January.
    10. Bo Xu & Hongfei Lin & Yang Chen & Zhihao Yang & Hongfang Liu, 2013. "Protein Complex Identification by Integrating Protein-Protein Interaction Evidence from Multiple Sources," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-12, December.
    11. Miguel Fribourg & Diomedes E Logothetis & Javier González-Maeso & Stuart C Sealfon & Belén Galocha-Iragüen & Fernando Las-Heras Andrés & Vladimir Brezina, 2017. "Elucidation of molecular kinetic schemes from macroscopic traces using system identification," PLOS Computational Biology, Public Library of Science, vol. 13(2), pages 1-34, February.
    12. Scholtens, Denise & Miron, Alexander & M. Merchant, Faisal & Miller, Arden & L. Miron, Penelope & Dirk Iglehart, J. & Gentleman, Robert, 2004. "Analyzing factorial designed microarray experiments," Journal of Multivariate Analysis, Elsevier, vol. 90(1), pages 19-43, July.
    13. Robrecht Cannoodt & Joeri Ruyssinck & Jan Ramon & Katleen De Preter & Yvan Saeys, 2018. "IncGraph: Incremental graphlet counting for topology optimisation," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-11, April.
    14. Margaritis Voliotis & Philipp Thomas & Ramon Grima & Clive G Bowsher, 2016. "Stochastic Simulation of Biomolecular Networks in Dynamic Environments," PLOS Computational Biology, Public Library of Science, vol. 12(6), pages 1-18, June.
    15. Pilar García-Peñarrubia & Juan J Gálvez & Jesús Gálvez, 2011. "Spatio-Temporal Dependence of the Signaling Response in Immune-Receptor Trafficking Networks Regulated by Cell Density: A Theoretical Model," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-12, July.
    16. Emre Guney & Baldo Oliva, 2014. "Analysis of the Robustness of Network-Based Disease-Gene Prioritization Methods Reveals Redundancy in the Human Interactome and Functional Diversity of Disease-Genes," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-11, April.
    17. Cui, Xue-Mei & Yoon, Chang No & Youn, Hyejin & Lee, Sang Hoon & Jung, Jean S. & Han, Seung Kee, 2017. "Dynamic burstiness of word-occurrence and network modularity in textbook systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 487(C), pages 103-110.
    18. Zheng Hong, 2019. "A novel individualized drug repositioning approach for predicting personalized candidate drugs for type 1 diabetes mellitus," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(5), pages 1-9, October.
    19. Zhang, Yuerong & Marshall, Stephen & Manley, Ed, 2021. "Understanding the roles of rail stations: Insights from network approaches in the London metropolitan area," Journal of Transport Geography, Elsevier, vol. 94(C).
    20. Perc, Matjaž, 2010. "Growth and structure of Slovenia’s scientific collaboration network," Journal of Informetrics, Elsevier, vol. 4(4), pages 475-482.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0072366. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.