Reverse engineering gene regulatory networks using approximate Bayesian computation

Andrea Rau 1, 2, 3 Florence Jaffrézic 2 Jean Louis Foulley 2 R. W. Doerge 3
1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : Gene regulatory networks are collections of genes that interact with one other and with other substances in the cell. By measuring gene expression over time using high-throughput technologies, it may be possible to reverse engineer, or infer, the structure of the gene network involved in a particular cellular process. These gene expression data typically have a high dimensionality and a limited number of biological replicates and time points. Due to these issues and the complexity of biological systems, the problem of reverse engineering networks from gene expression data demands a specialized suite of statistical tools and methodologies. We propose a non-standard adaptation of a simulation-based approach known as Approximate Bayesian Computing based on Markov chain Monte Carlo sampling. This approach is particularly well suited for the inference of gene regulatory networks from longitudinal data. The performance of this approach is investigated via simulations and using longitudinal expression data from a genetic repair system in Escherichia coli.
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Article dans une revue
Statistics and Computing, Springer Verlag (Germany), 2012, 22 (6), pp.1257-1271. 〈10.1007/s11222-011-9309-1〉
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https://hal.inria.fr/hal-00942725
Contributeur : Erwan Le Pennec <>
Soumis le : jeudi 6 février 2014 - 12:22:28
Dernière modification le : vendredi 23 mars 2018 - 11:42:28

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Andrea Rau, Florence Jaffrézic, Jean Louis Foulley, R. W. Doerge. Reverse engineering gene regulatory networks using approximate Bayesian computation. Statistics and Computing, Springer Verlag (Germany), 2012, 22 (6), pp.1257-1271. 〈10.1007/s11222-011-9309-1〉. 〈hal-00942725〉

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