Jean-Louis Foulley - Academia.edu (original) (raw)

Jean-Louis Foulley

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Papers by Jean-Louis Foulley

Research paper thumbnail of A link function approach to heterogeneous variance components

Genetics Selection Evolution, 1998

Research paper thumbnail of EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis (EM algorithm, random regression)

HAL (Le Centre pour la Communication Scientifique Directe), 2000

Research paper thumbnail of EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis

Genetics Selection Evolution, 2000

Research paper thumbnail of Genetic analysis of growth curves using the SAEM algorithm

Genetics Selection Evolution, Nov 28, 2006

Research paper thumbnail of Heteroskedastic random coefficient models

Research paper thumbnail of Genetic improvement of litter size in sheep. A comparison of selection methods

Genetics Selection Evolution, 1995

Research paper thumbnail of Relations a priori entre BLUP méthode de comparaison aux contemporaines et méthodes des différences cumulées en vue de l'évaluation des pères

Genetics Selection Evolution, 1977

Research paper thumbnail of Méthodes mathématiques pour l'étude des gènes contrôlant des caractères quantitatifs

Genetics Selection Evolution, 1994

Research paper thumbnail of EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis

Genetics Selection Evolution, 2000

Research paper thumbnail of Modelling the growth curve of Maine-Anjou beef cattle using heteroskedastic random coefficients models

Genetics Selection Evolution, 2002

Research paper thumbnail of Hétéroscédasticité et modèles linéaires mixtes: théorie et applications en génétique quantitative

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 ...

Research paper thumbnail of L’évaluation des reproducteurs : L’évaluation génétique des reproducteurs pour des caractères à seuil

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.

Research paper thumbnail of Statistical Approaches to Genetic Evaluation for Threshold Binary Traits

Research paper thumbnail of L'évaluation génétique des reproducteurs pour des caractères à seuil

Research paper thumbnail of A quasi-score approach to the analysis of ordered categorical data via a mixed heteroskedastic threshold model

Genetics Selection Evolution, 1999

Research paper thumbnail of Approximate Bayesian Approaches for Reverse Engineering Biological Networks

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.

Research paper thumbnail of A Bayesian Outlier Criterion to Detect SNPs under Selection in Large Data Sets

Research paper thumbnail of The Genome Response to Artificial Selection: A Case Study in Dairy Cattle

Research paper thumbnail of An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data

Statistical Applications in Genetics and Molecular Biology, 2010

Research paper thumbnail of Estimation of genetic merit from bivariate « all or none » responses

Genetics Selection Evolution, 1984

Research paper thumbnail of A link function approach to heterogeneous variance components

Genetics Selection Evolution, 1998

Research paper thumbnail of EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis (EM algorithm, random regression)

HAL (Le Centre pour la Communication Scientifique Directe), 2000

Research paper thumbnail of EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis

Genetics Selection Evolution, 2000

Research paper thumbnail of Genetic analysis of growth curves using the SAEM algorithm

Genetics Selection Evolution, Nov 28, 2006

Research paper thumbnail of Heteroskedastic random coefficient models

Research paper thumbnail of Genetic improvement of litter size in sheep. A comparison of selection methods

Genetics Selection Evolution, 1995

Research paper thumbnail of Relations a priori entre BLUP méthode de comparaison aux contemporaines et méthodes des différences cumulées en vue de l'évaluation des pères

Genetics Selection Evolution, 1977

Research paper thumbnail of Méthodes mathématiques pour l'étude des gènes contrôlant des caractères quantitatifs

Genetics Selection Evolution, 1994

Research paper thumbnail of EM-REML estimation of covariance parameters in Gaussian mixed models for longitudinal data analysis

Genetics Selection Evolution, 2000

Research paper thumbnail of Modelling the growth curve of Maine-Anjou beef cattle using heteroskedastic random coefficients models

Genetics Selection Evolution, 2002

Research paper thumbnail of Hétéroscédasticité et modèles linéaires mixtes: théorie et applications en génétique quantitative

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 ...

Research paper thumbnail of L’évaluation des reproducteurs : L’évaluation génétique des reproducteurs pour des caractères à seuil

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.

Research paper thumbnail of Statistical Approaches to Genetic Evaluation for Threshold Binary Traits

Research paper thumbnail of L'évaluation génétique des reproducteurs pour des caractères à seuil

Research paper thumbnail of A quasi-score approach to the analysis of ordered categorical data via a mixed heteroskedastic threshold model

Genetics Selection Evolution, 1999

Research paper thumbnail of Approximate Bayesian Approaches for Reverse Engineering Biological Networks

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.

Research paper thumbnail of A Bayesian Outlier Criterion to Detect SNPs under Selection in Large Data Sets

Research paper thumbnail of The Genome Response to Artificial Selection: A Case Study in Dairy Cattle

Research paper thumbnail of An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data

Statistical Applications in Genetics and Molecular Biology, 2010

Research paper thumbnail of Estimation of genetic merit from bivariate « all or none » responses

Genetics Selection Evolution, 1984

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