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Jean-Michel Marin

Showing results (1-10 of 12) with videos related to

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Proceedings of the National Academy of Sciences of the United States of America|August 31, 2011
Lack of confidence in approximate Bayesian computation model choiceChristian P Robert, Jean-Marie Cornuet, Jean-Michel Marin, et al.
Bioinformatics (Oxford, England)|October 16, 2018
ABC random forests for Bayesian parameter inferenceLouis Raynal, Jean-Michel Marin, Pierre Pudlo, et al.
Bioinformatics (Oxford, England)|November 22, 2015
Reliable ABC model choice via random forestsPierre Pudlo, Jean-Michel Marin, Arnaud Estoup, et al.
Genome Biology|July 10, 2024
TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factorsRaphaël Romero, Christophe Menichelli, Christophe Vroland, et al.
Molecular Ecology|October 1, 2020
A young age of subspecific divergence in the desert locust inferred by ABC random forestMarie-Pierre Chapuis, Louis Raynal, Christophe Plantamp, et al.
Molecular Ecology Resources|May 11, 2012
Estimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statisticsArnaud Estoup, Eric Lombaert, Jean-Michel Marin, et al.
Plos Computational Biology|January 3, 2018
Probing instructions for expression regulation in gene nucleotide compositionsChloé Bessière, May Taha, Florent Petitprez, et al.
Nature Structural & Molecular Biology|July 4, 2012
Unraveling cell type-specific and reprogrammable human replication origin signatures associated with G-quadruplex consensus motifsEmilie Besnard, Amélie Babled, Laure Lapasset, et al.
Molecular Ecology Resources|May 5, 2021
Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random ForestFrançois-David Collin, Ghislain Durif, Louis Raynal, et al.
Bioinformatics (Oxford, England)|January 7, 2014
DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite dataJean-Marie Cornuet, Pierre Pudlo, Julien Veyssier, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Proceedings of the National Academy of Sciences of the United States of America|August 31, 2011
Lack of confidence in approximate Bayesian computation model choiceChristian P Robert, Jean-Marie Cornuet, Jean-Michel Marin, et al.
Bioinformatics (Oxford, England)|October 16, 2018
ABC random forests for Bayesian parameter inferenceLouis Raynal, Jean-Michel Marin, Pierre Pudlo, et al.
Bioinformatics (Oxford, England)|November 22, 2015
Reliable ABC model choice via random forestsPierre Pudlo, Jean-Michel Marin, Arnaud Estoup, et al.
Genome Biology|July 10, 2024
TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factorsRaphaël Romero, Christophe Menichelli, Christophe Vroland, et al.
Molecular Ecology|October 1, 2020
A young age of subspecific divergence in the desert locust inferred by ABC random forestMarie-Pierre Chapuis, Louis Raynal, Christophe Plantamp, et al.
Molecular Ecology Resources|May 11, 2012
Estimation of demo-genetic model probabilities with Approximate Bayesian Computation using linear discriminant analysis on summary statisticsArnaud Estoup, Eric Lombaert, Jean-Michel Marin, et al.
Plos Computational Biology|January 3, 2018
Probing instructions for expression regulation in gene nucleotide compositionsChloé Bessière, May Taha, Florent Petitprez, et al.
Nature Structural & Molecular Biology|July 4, 2012
Unraveling cell type-specific and reprogrammable human replication origin signatures associated with G-quadruplex consensus motifsEmilie Besnard, Amélie Babled, Laure Lapasset, et al.
Molecular Ecology Resources|May 5, 2021
Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random ForestFrançois-David Collin, Ghislain Durif, Louis Raynal, et al.
Bioinformatics (Oxford, England)|January 7, 2014
DIYABC v2.0: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite dataJean-Marie Cornuet, Pierre Pudlo, Julien Veyssier, et al.
Pageof 2