IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0012789.html
   My bibliography  Save this article

A Boolean Approach to Linear Prediction for Signaling Network Modeling

Author

Listed:
  • Federica Eduati
  • Alberto Corradin
  • Barbara Di Camillo
  • Gianna Toffolo

Abstract

The task of the DREAM4 (Dialogue for Reverse Engineering Assessments and Methods) “Predictive signaling network modeling” challenge was to develop a method that, from single-stimulus/inhibitor data, reconstructs a cause-effect network to be used to predict the protein activity level in multi-stimulus/inhibitor experimental conditions. The method presented in this paper, one of the best performing in this challenge, consists of 3 steps: 1. Boolean tables are inferred from single-stimulus/inhibitor data to classify whether a particular combination of stimulus and inhibitor is affecting the protein. 2. A cause-effect network is reconstructed starting from these tables. 3. Training data are linearly combined according to rules inferred from the reconstructed network. This method, although simple, permits one to achieve a good performance providing reasonable predictions based on a reconstructed network compatible with knowledge from the literature. It can be potentially used to predict how signaling pathways are affected by different ligands and how this response is altered by diseases.

Suggested Citation

  • Federica Eduati & Alberto Corradin & Barbara Di Camillo & Gianna Toffolo, 2010. "A Boolean Approach to Linear Prediction for Signaling Network Modeling," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-6, September.
    • Handle: RePEc:plo:pone00:0012789
    DOI: 10.1371/journal.pone.0012789
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0012789
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0012789&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0012789?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. Alexander Mitsos & Ioannis N Melas & Paraskeuas Siminelakis & Aikaterini D Chairakaki & Julio Saez-Rodriguez & Leonidas G Alexopoulos, 2009. "Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data," PLOS Computational Biology, Public Library of Science, vol. 5(12), pages 1-11, 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. Qian Wan & Ranadip Pal, 2014. "An Ensemble Based Top Performing Approach for NCI-DREAM Drug Sensitivity Prediction Challenge," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-12, June.
    2. Stefan Balabanov & Thomas Wilhelm & Simone Venz & Gunhild Keller & Christian Scharf & Heike Pospisil & Melanie Braig & Christine Barett & Carsten Bokemeyer & Reinhard Walther & Tim H Brümmendorf & And, 2013. "Combination of a Proteomics Approach and Reengineering of Meso Scale Network Models for Prediction of Mode-of-Action for Tyrosine Kinase Inhibitors," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-14, January.
    3. Zhiwei Ji & Jing Su & Chenglin Liu & Hongyan Wang & Deshuang Huang & Xiaobo Zhou, 2014. "Integrating Genomics and Proteomics Data to Predict Drug Effects Using Binary Linear Programming," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-13, July.
    4. Ioannis N Melas & Regina Samaga & Leonidas G Alexopoulos & Steffen Klamt, 2013. "Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs," PLOS Computational Biology, Public Library of Science, vol. 9(9), pages 1-19, September.
    5. Zebian, Hussam & Mitsos, Alexander, 2013. "Pressurized oxy-coal combustion: Ideally flexible to uncertainties," Energy, Elsevier, vol. 57(C), pages 513-526.
    6. Melody K Morris & Julio Saez-Rodriguez & David C Clarke & Peter K Sorger & Douglas A Lauffenburger, 2011. "Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-20, March.

    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:pone00:0012789. 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.