IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0094686.html
   My bibliography  Save this article

Analysis of the Robustness of Network-Based Disease-Gene Prioritization Methods Reveals Redundancy in the Human Interactome and Functional Diversity of Disease-Genes

Author

Listed:
  • Emre Guney
  • Baldo Oliva

Abstract

Complex biological systems usually pose a trade-off between robustness and fragility where a small number of perturbations can substantially disrupt the system. Although biological systems are robust against changes in many external and internal conditions, even a single mutation can perturb the system substantially, giving rise to a pathophenotype. Recent advances in identifying and analyzing the sequential variations beneath human disorders help to comprehend a systemic view of the mechanisms underlying various disease phenotypes. Network-based disease-gene prioritization methods rank the relevance of genes in a disease under the hypothesis that genes whose proteins interact with each other tend to exhibit similar phenotypes. In this study, we have tested the robustness of several network-based disease-gene prioritization methods with respect to the perturbations of the system using various disease phenotypes from the Online Mendelian Inheritance in Man database. These perturbations have been introduced either in the protein-protein interaction network or in the set of known disease-gene associations. As the network-based disease-gene prioritization methods are based on the connectivity between known disease-gene associations, we have further used these methods to categorize the pathophenotypes with respect to the recoverability of hidden disease-genes. Our results have suggested that, in general, disease-genes are connected through multiple paths in the human interactome. Moreover, even when these paths are disturbed, network-based prioritization can reveal hidden disease-gene associations in some pathophenotypes such as breast cancer, cardiomyopathy, diabetes, leukemia, parkinson disease and obesity to a greater extend compared to the rest of the pathophenotypes tested in this study. Gene Ontology (GO) analysis highlighted the role of functional diversity for such diseases.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0094686
    DOI: 10.1371/journal.pone.0094686
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0094686
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0094686&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0094686?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. 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.
    17. 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.
    18. 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).
    19. Perc, Matjaž, 2010. "Growth and structure of Slovenia’s scientific collaboration network," Journal of Informetrics, Elsevier, vol. 4(4), pages 475-482.
    20. Krivonosov, Mikhail & Nazarenko, Tatiana & Bacalini, Maria Giulia & Vedunova, Maria & Franceschi, Claudio & Zaikin, Alexey & Ivanchenko, Mikhail, 2022. "Age-related trajectories of DNA methylation network markers: A parenclitic network approach to a family-based cohort of patients with Down Syndrome," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).

    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:pone00:0094686. 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.