A novel procedure to determine differentially expressed genes between two conditions, and an application to the comparison of two bovine gestation modes

Autor: Mylene Duval, Severine Degrelle, Céline Delmas, Isabelle Hue, Laurent, B., Christèle Robert-Granié

Publikováno v: 8. World Congress on Genetics Applied to Livestock Production
8. World Congress on Genetics Applied to Livestock Production, Aug 2006, Belo Horizonte, Brazil. 1 p
HAL

International audience

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::4ddcfdc7a5ec45df6db0595878802bf2
https://hal.inrae.fr/hal-02816165

Analysis of the real EADGENE data set : Multivariate approaches and post analysis

Autor: Peter Sorensen, Agnès Bonnet, Bart Buitenhuis, Rodrigue Closset, Sébastien Dejean, Céline Delmas, Mylene Duval, Liz Glass, Jakob Hedegaard, Henrik Hornshoj, Ina Hulsegge, Florence Jaffrezic, Kirsty Jensen, Li Jiang, Dirk-Jan Koning, Kim-Anh Lê Cao, Haisheng Nie, Wolfram Petzl, Pool, M. H., Christèle Robert-Granié, Magali San Cristobal, Mogens Sando Lund, Schothorst, E. M., Hans-Joachim Schuberth, Hans-Martin Seyfert, Gwenola Tosser-Klopp, David Waddington, Michael Watson, Wei Yang, Holm Zerbe

Publikováno v: HAL
Genetics Selection Evolution
Genetics Selection Evolution, BioMed Central, 2007, 39 (6), pp.651-668
Genetics Selection Evolution 6 (39), 651-668. (2007)

International audience; The aim of this paper was to describe, and when possible compare, the multivariate methods used by the participants in the EADGENE WP1.4 workshop. The first approach was for class discovery and class prediction using evidence

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::5238971f9dc9f7162017fd7c0257342e
https://hal.inrae.fr/hal-02658153

Estimation of heterogeneous variances in nonlinear mixed models via the SAEM-MCMC algorithm with applications to growth curves in poultry

Autor: Mylene Duval, Christian Robert, Jean Louis Foulley

Publikováno v: HAL
Journal de la Société Française de Statistique
Journal de la Société Française de Statistique, Société Française de Statistique et Société Mathématique de France, 2009, 150 (2), pp.65-83

The SAEM-MCMC algorithm is a powerful tool for computing maximum likelihood estimators in the wide class of nonlinear mixed effects models. We propose in this article an adaptation of this algorithm to the estimation of heterogeneous variances in suc

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::c7169558d9ead2d9a746de887855968d
https://hal.archives-ouvertes.fr/hal-01193504