IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v387y2008i4p1049-1055.html
   My bibliography  Save this article

In silico network topology-based prediction of gene essentiality

Author

Listed:
  • da Silva, João Paulo Müller
  • Acencio, Marcio Luis
  • Mombach, José Carlos Merino
  • Vieira, Renata
  • da Silva, José Camargo
  • Lemke, Ney
  • Sinigaglia, Marialva

Abstract

The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance.

Suggested Citation

  • da Silva, João Paulo Müller & Acencio, Marcio Luis & Mombach, José Carlos Merino & Vieira, Renata & da Silva, José Camargo & Lemke, Ney & Sinigaglia, Marialva, 2008. "In silico network topology-based prediction of gene essentiality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 1049-1055.
    • Handle: RePEc:eee:phsmap:v:387:y:2008:i:4:p:1049-1055
    DOI: 10.1016/j.physa.2007.10.044
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437107011417
    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.2007.10.044?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Qian & Qi, Saibing & Ren, Cong, 2021. "Gene essentiality prediction based on chaos game representation and spiking neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    2. Marcio Luis Acencio & Luiz Augusto Bovolenta & Esther Camilo & Ney Lemke, 2013. "Prediction of Oncogenic Interactions and Cancer-Related Signaling Networks Based on Network Topology," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-12, October.

    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:387:y:2008:i:4:p:1049-1055. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.