Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data.

Thomas Guillemaud

dblp:74/7065 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 2008
0000-0003-0451-1644ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology
approximate bayesian computation
0.112008
Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation · Bioinform. 2008
Bioinformatics and computational biology › population genetics › population parameter estimation
demographic inference
0.112008
Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation · Bioinform. 2008
Bioinformatics and computational biology
population genetics
0.112008
Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation · Bioinform. 2008

Methods — techniques the papers use, named apart from their topics

parameter estimation · 0.1bias and precision estimation · 0.1approximate bayesian computation · 0.1
YearPublicationVenuePosition
2008 Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation
abstract
UNLABELLED: Genetic data obtained on population samples convey information about their evolutionary history. Inference methods can extract part of this information but they require sophisticated statistical techniques that have been made available to the biologist community (through computer programs) only for simple and standard situations typically involving a small number of samples. We propose here a computer program (DIY ABC) for inference based on approximate Bayesian computation (ABC), in which scenarios can be customized by the user to fit many complex situations involving any number of populations and samples. Such scenarios involve any combination of population divergences, admixtures and population size changes. DIY ABC can be used to compare competing scenarios, estimate parameters for one or more scenarios and compute bias and precision measures for a given scenario and known values of parameters (the current version applies to unlinked microsatellite data). This article describes key methods used in the program and provides its main features. The analysis of one simulated and one real dataset, both with complex evolutionary scenarios, illustrates the main possibilities of DIY ABC. AVAILABILITY: The software DIY ABC is freely available at http://www.montpellier.inra.fr/CBGP/diyabc.
Jean-Marie Cornuet, Mark A. Beaumont, Christian P. Robert, Jean-Michel Marin, David J. Balding, Thomas Guillemaud, Arnaud Estoup
Bioinform.7