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A Full Bayesian Approach for Boolean Genetic Network Inference

Author

Listed:
  • Shengtong Han
  • Raymond K W Wong
  • Thomas C M Lee
  • Linghao Shen
  • Shuo-Yen R Li
  • Xiaodan Fan

Abstract

Boolean networks are a simple but efficient model for describing gene regulatory systems. A number of algorithms have been proposed to infer Boolean networks. However, these methods do not take full consideration of the effects of noise and model uncertainty. In this paper, we propose a full Bayesian approach to infer Boolean genetic networks. Markov chain Monte Carlo algorithms are used to obtain the posterior samples of both the network structure and the related parameters. In addition to regular link addition and removal moves, which can guarantee the irreducibility of the Markov chain for traversing the whole network space, carefully constructed mixture proposals are used to improve the Markov chain Monte Carlo convergence. Both simulations and a real application on cell-cycle data show that our method is more powerful than existing methods for the inference of both the topology and logic relations of the Boolean network from observed data.

Suggested Citation

  • Shengtong Han & Raymond K W Wong & Thomas C M Lee & Linghao Shen & Shuo-Yen R Li & Xiaodan Fan, 2014. "A Full Bayesian Approach for Boolean Genetic Network Inference," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-13, December.
    • Handle: RePEc:plo:pone00:0115806
    DOI: 10.1371/journal.pone.0115806
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    Cited by:

    1. Shohag Barman & Yung-Keun Kwon, 2017. "A novel mutual information-based Boolean network inference method from time-series gene expression data," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-19, February.
    2. Zhang, Hongmei & Huang, Xianzheng & Han, Shengtong & Rezwan, Faisal I. & Karmaus, Wilfried & Arshad, Hasan & Holloway, John W., 2021. "Gaussian Bayesian network comparisons with graph ordering unknown," Computational Statistics & Data Analysis, Elsevier, vol. 157(C).

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