Jean-Louis Foulley - Academia.edu (original) (raw)
Related Authors
Saint-Petersburg Stieglitz State Academy of Art and Design
Rheinische Friedrich-Wilhelms-Universität Bonn
Uploads
Papers by Jean-Louis Foulley
Genetics Selection Evolution, 1998
HAL (Le Centre pour la Communication Scientifique Directe), 2000
Genetics Selection Evolution, 2000
Genetics Selection Evolution, Nov 28, 2006
Genetics Selection Evolution, 1995
Genetics Selection Evolution, 1977
Genetics Selection Evolution, 1994
Genetics Selection Evolution, 2000
Genetics Selection Evolution, 2002
Journal de la Société …, 2002
Cet article montre comment a été introduit et formalisé le concept de variances hétérogènes en gé... more Cet article montre comment a été introduit et formalisé le concept de variances hétérogènes en génétique animale par le biais d'une modélisation à effets mixtes du logarithme des variances. Divers modèles sont présentés et discutés. L'ensemble est illustré à travers ...
INRAE Productions Animales, 1992
Cet article rappelle les principales caractéristiques du modèle à seuils de Sewall Wright applica... more Cet article rappelle les principales caractéristiques du modèle à seuils de Sewall Wright applicable aux variables discrètes binaires et polytomiques ordonnées ainsi que ses principaux domaines d’application notamment en génétique et sélection animale. En prenant l’exemple d’un caractère dichotomique, on montre que l’analyse statistique de ces caractères rentre dans le cadre de la théorie du modèle linéaire généralisé de Mc Cullagh et Nelder. On mentionne ensuite l’approche bayésienne de Gianola et Foulley d’évaluation des reproducteurs. Diverses extensions sont enfin discutées.
Genetics Selection Evolution, 1999
Conference on Applied Statistics in Agriculture, 2010
Genes are known to interact with one another through proteins by regulating the rate at which gen... more Genes are known to interact with one another through proteins by regulating the rate at which gene transcription takes place. As such, identifying these gene-to-gene interactions is essential to improving our knowledge of how complex biological systems work. In recent years, a growing body of work has focused on methods for reverse-engineering these so-called gene regulatory networks from time-course gene expression data. However, reconstruction of these networks is often complicated by the large number of genes potentially involved in a given network and the limited number of time points and biological replicates typically measured. Bayesian methods are particularly well-suited for dealing with problems of this nature, as they provide a systematic way to deal with different sources of variation and allow for a measure of uncertainty in parameter estimates through posterior distributions, rather than point estimates. Our current work examines the application of approximate Bayesian methodology for the purpose of reverse engineering regulatory networks from time-course gene expression data. We demonstrate the advantages of our proposed approximate Bayesian approaches by comparing their performance on a well-characterized pathway in Escherichia coli.
Statistical Applications in Genetics and Molecular Biology, 2010
Genetics Selection Evolution, 1984
Genetics Selection Evolution, 1998
HAL (Le Centre pour la Communication Scientifique Directe), 2000
Genetics Selection Evolution, 2000
Genetics Selection Evolution, Nov 28, 2006
Genetics Selection Evolution, 1995
Genetics Selection Evolution, 1977
Genetics Selection Evolution, 1994
Genetics Selection Evolution, 2000
Genetics Selection Evolution, 2002
Journal de la Société …, 2002
Cet article montre comment a été introduit et formalisé le concept de variances hétérogènes en gé... more Cet article montre comment a été introduit et formalisé le concept de variances hétérogènes en génétique animale par le biais d'une modélisation à effets mixtes du logarithme des variances. Divers modèles sont présentés et discutés. L'ensemble est illustré à travers ...
INRAE Productions Animales, 1992
Cet article rappelle les principales caractéristiques du modèle à seuils de Sewall Wright applica... more Cet article rappelle les principales caractéristiques du modèle à seuils de Sewall Wright applicable aux variables discrètes binaires et polytomiques ordonnées ainsi que ses principaux domaines d’application notamment en génétique et sélection animale. En prenant l’exemple d’un caractère dichotomique, on montre que l’analyse statistique de ces caractères rentre dans le cadre de la théorie du modèle linéaire généralisé de Mc Cullagh et Nelder. On mentionne ensuite l’approche bayésienne de Gianola et Foulley d’évaluation des reproducteurs. Diverses extensions sont enfin discutées.
Genetics Selection Evolution, 1999
Conference on Applied Statistics in Agriculture, 2010
Genes are known to interact with one another through proteins by regulating the rate at which gen... more Genes are known to interact with one another through proteins by regulating the rate at which gene transcription takes place. As such, identifying these gene-to-gene interactions is essential to improving our knowledge of how complex biological systems work. In recent years, a growing body of work has focused on methods for reverse-engineering these so-called gene regulatory networks from time-course gene expression data. However, reconstruction of these networks is often complicated by the large number of genes potentially involved in a given network and the limited number of time points and biological replicates typically measured. Bayesian methods are particularly well-suited for dealing with problems of this nature, as they provide a systematic way to deal with different sources of variation and allow for a measure of uncertainty in parameter estimates through posterior distributions, rather than point estimates. Our current work examines the application of approximate Bayesian methodology for the purpose of reverse engineering regulatory networks from time-course gene expression data. We demonstrate the advantages of our proposed approximate Bayesian approaches by comparing their performance on a well-characterized pathway in Escherichia coli.
Statistical Applications in Genetics and Molecular Biology, 2010
Genetics Selection Evolution, 1984