IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0051141.html
   My bibliography  Save this article

Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

Author

Listed:
  • Ming Zheng
  • Jia-nan Wu
  • Yan-xin Huang
  • Gui-xia Liu
  • You Zhou
  • Chun-guang Zhou

Abstract

Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms.

Suggested Citation

  • Ming Zheng & Jia-nan Wu & Yan-xin Huang & Gui-xia Liu & You Zhou & Chun-guang Zhou, 2012. "Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm," PLOS ONE, Public Library of Science, vol. 7(12), pages 1-6, December.
  • Handle: RePEc:plo:pone00:0051141
    DOI: 10.1371/journal.pone.0051141
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0051141
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0051141&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0051141?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. Matthieu Vignes & Jimmy Vandel & David Allouche & Nidal Ramadan-Alban & Christine Cierco-Ayrolles & Thomas Schiex & Brigitte Mangin & Simon de Givry, 2011. "Gene Regulatory Network Reconstruction Using Bayesian Networks, the Dantzig Selector, the Lasso and Their Meta-Analysis," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-15, December.
    2. Haisun Zhu & Rajanikanth Vadigepalli & Rachel Rafferty & Gregory E Gonye & David R Weaver & James S Schwaber, 2012. "Integrative Gene Regulatory Network Analysis Reveals Light-Induced Regional Gene Expression Phase Shift Programs in the Mouse Suprachiasmatic Nucleus," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-14, May.
    3. Ou, Ruiqiu & Yang, Jianmei, 2012. "On structural properties of scale-free networks with finite size," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 887-894.
    4. Nazar Zaki & Salah Bouktif & Sanja Lazarova-Molnar, 2011. "A Combination of Compositional Index and Genetic Algorithm for Predicting Transmembrane Helical Segments," PLOS ONE, Public Library of Science, vol. 6(7), pages 1-8, July.
    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. Almudevar, Anthony, 2016. "An information theoretic approach to pedigree reconstruction," Theoretical Population Biology, Elsevier, vol. 107(C), pages 52-64.
    2. Ming Zheng & Ying Sun & Gui-xia Liu & You Zhou & Chun-guang Zhou, 2012. "Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-10, November.
    3. Sanjana Gupta & Robin E C Lee & James R Faeder, 2020. "Parallel Tempering with Lasso for model reduction in systems biology," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-22, 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:pone00:0051141. 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.