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Analyzing Boolean Networks Through Unsupervised Learning

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Part of the Emergence, Complexity and Computation book series (ECC,volume 42)

Abstract

Boolean networks are typically used as simple models of gene regulatory networks. We use a particular class of Boolean networks called threshold Boolean networks defined by a weight matrix, a threshold vector, and an updating mode in this work. We consider the reconstruction of synthetic threshold Boolean networks that contain the same fixed points as the Mendoza and Alvarez-Buylla network of flower development by using an evolution strategy. We propose a characterization by computing topological and dynamical features of the inferred synthetic networks and then applying machine learning, particularly unsupervised learning techniques, to analyze these networks. We discover how these networks are clustered and what features are relevant to discriminate the cluster containing the Mendoza and Alvarez-Buylla network from all the other clusters.

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  • DOI: 10.1007/978-3-030-92551-2_14
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Acknowledgements

This work was funded by ANID FONDECYT 1180706 and ANID PIA/BASAL FB0002.

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Correspondence to Gonzalo A. Ruz .

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Ruz, G.A. (2022). Analyzing Boolean Networks Through Unsupervised Learning. In: Adamatzky, A. (eds) Automata and Complexity. Emergence, Complexity and Computation, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-030-92551-2_14

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  • DOI: https://doi.org/10.1007/978-3-030-92551-2_14

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-92551-2

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