Abstract
Gene regulatory network (GRN) models have been shown to predict and represent interactions among sets of genes. Here, we first show the basic steps to implement a simple but computationally efficient algorithm to infer GRNs based on dynamic Bayesian networks (DBNs), and we then explain how to approximate DBN-based GRN models with continuous models. In addition, we show a MATLAB implementation of the key steps of this method, which we use to infer an Arabidopsis root GRN.
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Acknowledgments
Support for this work was provided by the National Science Foundation (R.S.: NSF CAREER MCB 1453130).
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de Luis Balaguer, M.A., Sozzani, R. (2017). Inferring Gene Regulatory Networks in the Arabidopsis Root Using a Dynamic Bayesian Network Approach. In: Kaufmann, K., Mueller-Roeber, B. (eds) Plant Gene Regulatory Networks. Methods in Molecular Biology, vol 1629. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7125-1_21
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DOI: https://doi.org/10.1007/978-1-4939-7125-1_21
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Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-7124-4
Online ISBN: 978-1-4939-7125-1
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