ArticlePloS one2014
A full bayesian approach for boolean genetic network inference.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- From prior knowledge to data-informed models: a review of Boolean network inference.Briefings in bioinformatics · 2026Review
- Article
- Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.Briefings in bioinformatics · 2024Article
- Diversity of transgenes in sustainable management of insect pests.Transgenic research · 2023Review
- Review and assessment of Boolean approaches for inference of gene regulatory networks.Heliyon · 2022Review
- A novel constrained genetic algorithm-based Boolean network inference method from steady-state gene expression data.Bioinformatics (Oxford, England) · 2021Article
- Gaussian Bayesian network comparisons with graph ordering unknown.Computational statistics & data analysis · 2021Article
- Griffin: A Tool for Symbolic Inference of Synchronous Boolean Molecular Networks.Frontiers in genetics · 2018Article
- Inferring transcriptional logic from multiple dynamic experiments.Bioinformatics (Oxford, England) · 2017Article
- An efficient Bayesian approach for Gaussian Bayesian network structure learning.Communications in statistics: Simulation and computation · 2017Article
- A novel mutual information-based Boolean network inference method from time-series gene expression data.PloS one · 2017Article
- Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty.PloS one · 2015Article
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Authors and funding
6 authors.
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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.
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