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Reverse Engineering of Gene Regulatory Networks Combining Dynamic Bayesian Networks and Prior Biological Knowledge

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

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Abstract

An important problem in the Systems Biology field is the reverse engineering of gene regulatory networks from gene expression data. In this work, we addressed this problem using a probabilistic graphical model known as Dynamic Bayesian Network to model the regulatory relations among the genes. We also used a Boolean formalism, assuming that each gene can take on two possible values: 0 (not expressed) and 1 (expressed). To learn the Dynamic Bayesian Network from time-series gene expression data we search for the network structure that best matches the data using the Bayesian Information Criterion score and the BDe score and compared them. Besides that, we used a source of prior biological knowledge from a database named STRING, unlike most of the reverse engineering algorithms that does not take into account any source of additional information. The results show that this approach can improve the quality of the inferred networks, and we also showed that the Dynamic Bayesian Network performs better than its standard version, Bayesian Network.

Supported by CNPq and UFMS.

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Acknowledgement

This research was supported by CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) and UFMS (Universidade Federal de Mato Grosso do Sul).

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Authors and Affiliations

  1. Federal University of Mato Grosso do Sul, Campo Grande, MS, 79070-900, Brazil

    Mariana C. de Souza & Carlos H. A. Higa

Authors
  1. Mariana C. de Souza
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  2. Carlos H. A. Higa
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Corresponding author

Correspondence to Carlos H. A. Higa .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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de Souza, M.C., Higa, C.H.A. (2018). Reverse Engineering of Gene Regulatory Networks Combining Dynamic Bayesian Networks and Prior Biological Knowledge. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10960. Springer, Cham. https://doi.org/10.1007/978-3-319-95162-1_22

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  • DOI: https://doi.org/10.1007/978-3-319-95162-1_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95161-4

  • Online ISBN: 978-3-319-95162-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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