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Weighted-LASSO for Structured Network Inference from Time Course Data

Author

Listed:
  • Charbonnier Camille

    (University of Évry-Val-d’Essonne)

  • Chiquet Julien

    (University of Évry-Val-d’Essonne)

  • Ambroise Christophe

    (University of Évry-Val-d’Essonne)

Abstract

We present a weighted-LASSO method to infer the parameters of a first-order vector auto-regressive model that describes time course expression data generated by directed gene-to-gene regulation networks. These networks are assumed to own prior internal structures of connectivity which drive the inference method. This prior structure can be either derived from prior biological knowledge or inferred by the method itself. We illustrate the performance of this structure-based penalization both on synthetic data and on two canonical regulatory networks (the yeast cell cycle regulation network and the E. coli S.O.S. DNA repair network).

Suggested Citation

  • Charbonnier Camille & Chiquet Julien & Ambroise Christophe, 2010. "Weighted-LASSO for Structured Network Inference from Time Course Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-29, February.
    • Handle: RePEc:bpj:sagmbi:v:9:y:2010:i:1:n:15
    DOI: 10.2202/1544-6115.1519
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    References listed on IDEAS

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    Cited by:

    1. Bergersen Linn Cecilie & Glad Ingrid K. & Lyng Heidi, 2011. "Weighted Lasso with Data Integration," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-29, August.
    2. McGillivray, Annaliza & Khalili, Abbas & Stephens, David A., 2020. "Estimating sparse networks with hubs," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    3. Ajmal Hamda B. & Madden Michael G., 2020. "Inferring dynamic gene regulatory networks with low-order conditional independencies – an evaluation of the method," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 19(4-6), pages 1-19, December.

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