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Gene Regulatory Networks pp 195–215Cite as

Unsupervised Gene Network Inference with Decision Trees and Random Forests

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1883))

Abstract

In this chapter, we introduce the reader to a popular family of machine learning algorithms, called decision trees. We then review several approaches based on decision trees that have been developed for the inference of gene regulatory networks (GRNs). Decision trees have indeed several nice properties that make them well-suited for tackling this problem: they are able to detect multivariate interacting effects between variables, are non-parametric, have good scalability, and have very few parameters. In particular, we describe in detail the GENIE3 algorithm, a state-of-the-art method for GRN inference.

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Acknowledgements

VAHT is a Post-doctoral Fellow of the F.R.S.-FNRS.

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    Vân Anh Huynh-Thu & Pierre Geurts

Authors
  1. Vân Anh Huynh-Thu
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  2. Pierre Geurts
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Corresponding author

Correspondence to Vân Anh Huynh-Thu .

Editor information

Editors and Affiliations

  1. School of Informatics, University of Edinburgh, Edinburgh, UK

    Guido Sanguinetti

  2. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

    Vân Anh Huynh-Thu

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Huynh-Thu, V.A., Geurts, P. (2019). Unsupervised Gene Network Inference with Decision Trees and Random Forests. In: Sanguinetti, G., Huynh-Thu, V. (eds) Gene Regulatory Networks. Methods in Molecular Biology, vol 1883. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8882-2_8

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  • DOI: https://doi.org/10.1007/978-1-4939-8882-2_8

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8881-5

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