IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v458y2016icp313-328.html
   My bibliography  Save this article

Gene-based and semantic structure of the Gene Ontology as a complex network

Author

Listed:
  • Coronnello, Claudia
  • Tumminello, Michele
  • Miccichè, Salvatore

Abstract

The last decade has seen the advent and consolidation of ontology based tools for the identification and biological interpretation of classes of genes, such as the Gene Ontology. The Gene Ontology (GO) is constantly evolving over time. The information accumulated time-by-time and included in the GO is encoded in the definition of terms and in the setting up of semantic relations amongst terms. Here we investigate the Gene Ontology from a complex network perspective. We consider the semantic network of terms naturally associated with the semantic relationships provided by the Gene Ontology consortium. Moreover, the GO is a natural example of bipartite network of terms and genes. Here we are interested in studying the properties of the projected network of terms, i.e. a gene-based weighted network of GO terms, in which a link between any two terms is set if at least one gene is annotated in both terms. One aim of the present paper is to compare the structural properties of the semantic and the gene-based network. The relative importance of terms is very similar in the two networks, but the community structure changes. We show that in some cases GO terms that appear to be distinct from a semantic point of view are instead connected, and appear in the same community when considering their gene content. The identification of such gene-based communities of terms might therefore be the basis of a simple protocol aiming at improving the semantic structure of GO. Information about terms that share large gene content might also be important from a biomedical point of view, as it might reveal how genes over-expressed in a certain term also affect other biological processes, molecular functions and cellular components not directly linked according to GO semantics.

Suggested Citation

  • Coronnello, Claudia & Tumminello, Michele & Miccichè, Salvatore, 2016. "Gene-based and semantic structure of the Gene Ontology as a complex network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 313-328.
  • Handle: RePEc:eee:phsmap:v:458:y:2016:i:c:p:313-328
    DOI: 10.1016/j.physa.2016.03.062
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116300607
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2016.03.062?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vasilis Hatzopoulos & Giulia Iori & Rosario N. Mantegna & Salvatore Miccich� & Michele Tumminello, 2015. "Quantifying preferential trading in the e-MID interbank market," Quantitative Finance, Taylor & Francis Journals, vol. 15(4), pages 693-710, April.
    2. Michele Tumminello & Salvatore Miccichè & Fabrizio Lillo & Jyrki Piilo & Rosario N Mantegna, 2011. "Statistically Validated Networks in Bipartite Complex Systems," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    3. Michele Tumminello & Christofer Edling & Fredrik Liljeros & Rosario N Mantegna & Jerzy Sarnecki, 2013. "The Phenomenology of Specialization of Criminal Suspects," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-8, May.
    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. Puccio, Elena & Pajala, Antti & Piilo, Jyrki & Tumminello, Michele, 2016. "Structure and evolution of a European Parliament via a network and correlation analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 167-185.
    2. Iori, Giulia & Mantegna, Rosario N. & Marotta, Luca & Miccichè, Salvatore & Porter, James & Tumminello, Michele, 2015. "Networked relationships in the e-MID interbank market: A trading model with memory," Journal of Economic Dynamics and Control, Elsevier, vol. 50(C), pages 98-116.
    3. Han, Rui-Qi & Li, Ming-Xia & Chen, Wei & Zhou, Wei-Xing & Stanley, H. Eugene, 2019. "Structural properties of statistically validated empirical information networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 747-756.
    4. Andrea Flori & Fabrizio Lillo & Fabio Pammolli & Alessandro Spelta, 2021. "Better to stay apart: asset commonality, bipartite network centrality, and investment strategies," Annals of Operations Research, Springer, vol. 299(1), pages 177-213, April.
    5. Ramadiah, Amanah & Caccioli, Fabio & Fricke, Daniel, 2020. "Reconstructing and stress testing credit networks," Journal of Economic Dynamics and Control, Elsevier, vol. 111(C).
    6. Tanskanen, Maiju & Aaltonen, Mikko, 2022. "Social correlates of specialized versus versatile offending patterns in intimate partner violence: A register-based study in Finland," Journal of Criminal Justice, Elsevier, vol. 81(C).
    7. Alessandro Ferracci & Giulio Cimini, 2021. "Systemic risk in interbank networks: disentangling balance sheets and network effects," Papers 2109.14360, arXiv.org, revised Sep 2022.
    8. Kobayashi, Teruyoshi & Takaguchi, Taro, 2018. "Identifying relationship lending in the interbank market: A network approach," Journal of Banking & Finance, Elsevier, vol. 97(C), pages 20-36.
    9. Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org.
    10. Sgrignoli, Paolo & Metulini, Rodolfo & Schiavo, Stefano & Riccaboni, Massimo, 2015. "The relation between global migration and trade networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 245-260.
    11. Corrêa, Edilson A. & Marinho, Vanessa Q. & Amancio, Diego R., 2020. "Semantic flow in language networks discriminates texts by genre and publication date," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    12. Nicoló Musmeci & Tomaso Aste & T Di Matteo, 2015. "Relation between Financial Market Structure and the Real Economy: Comparison between Clustering Methods," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-24, March.
    13. Carlo Campajola & Fabrizio Lillo & Daniele Tantari, 2019. "Unveiling the relation between herding and liquidity with trader lead-lag networks," Papers 1909.10807, arXiv.org, revised Mar 2020.
    14. Leonardo Bargigli, 2013. "Statistical Equilibrium Models for Sparse Economic Networks," Working Papers - Economics wp2013_25.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    15. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    16. Piero Mazzarisi & Silvia Zaoli & Carlo Campajola & Fabrizio Lillo, 2020. "Tail Granger causalities and where to find them: extreme risk spillovers vs. spurious linkages," Papers 2005.01160, arXiv.org, revised May 2021.
    17. Michele Tumminello & Fabrizio Lillo & Jyrki Piilo & Rosario N. Mantegna, 2011. "Identification of clusters of investors from their real trading activity in a financial market," Papers 1107.3942, arXiv.org.
    18. Anastasios Demertzidis, 2019. "Interbank transactions on the intraday frequency: -Different market states and the effects of the financial crisis-," MAGKS Papers on Economics 201932, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    19. Challet, Damien & Bongiorno, Christian & Pelletier, Guillaume, 2021. "Financial factors selection with knockoffs: Fund replication, explanatory and prediction networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
    20. Behrouzi, Saman & Shafaeipour Sarmoor, Zahra & Hajsadeghi, Khosrow & Kavousi, Kaveh, 2020. "Predicting scientific research trends based on link prediction in keyword networks," Journal of Informetrics, Elsevier, vol. 14(4).

    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:eee:phsmap:v:458:y:2016:i:c:p:313-328. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.