Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues

George Michailidis 1 Florence D'Alché-Buc 2, 3
3 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Reconstructing gene regulatory networks from high-throughput measurements represents a key problem in functional genomics. It also represents a canonical learning problem and thus has attracted a lot of attention in both the informatics and the statistical learning literature. Numerous approaches have been proposed, ranging from simple clustering to rather involved dynamic Bayesian network modeling, as well as hybrid ones that combine a number of modeling steps, such as employing ordinary differential equations coupled with genome annotation. These approaches are tailored to the type of data being employed. Available data sources include static steady state data and time course data obtained either for wild type phenotypes or from perturbation experiments. This review focuses on the class of autoregressive models using time course data for inferring gene regulatory networks. The central themes of sparsity, stability and causality are discussed as well as the ability to integrate prior knowledge for successful use of these models for the learning task at hand.
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Article dans une revue
Mathematical Biosciences, Elsevier, 2013, 246 (2), pp.326--334. 〈10.1016/j.mbs.2013.10.003〉
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Contributeur : Frédéric Davesne <>
Soumis le : mardi 26 novembre 2013 - 21:03:20
Dernière modification le : jeudi 7 février 2019 - 17:01:17

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George Michailidis, Florence D'Alché-Buc. Autoregressive models for gene regulatory network inference: Sparsity, stability and causality issues. Mathematical Biosciences, Elsevier, 2013, 246 (2), pp.326--334. 〈10.1016/j.mbs.2013.10.003〉. 〈hal-00909809〉

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