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Frédéric Y. Bois

dblp:35/7067 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2009
0000-0002-4154-0391ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

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
markov chain monte carlo
0.112009
GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models · Bioinform. 2009
Bioinformatics and computational biology
systems biology
0.112009
GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models · Bioinform. 2009
Bioinformatics and computational biology › systems biology › SBML
SBML model simulation
0.012009
GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models · Bioinform. 2009

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

hierarchical modeling · 0.1bayesian inference · 0.1MCMC · 0.1
YearPublicationVenuePosition
2009 GNU MCSim: Bayesian statistical inference for SBML-coded systems biology models
abstract
SUMMARY: Statistical inference about the parameter values of complex models, such as the ones routinely developed in systems biology, is efficiently performed through Bayesian numerical techniques. In that framework, prior information and multiple levels of uncertainty can be seamlessly integrated. GNU MCSim was precisely developed to achieve those aims, in a general non-linear differential context. Starting with version 5.3.0, GNU MCSim reads in and simulates Systems Biology Markup Language models. Markov chain Monte Carlo simulations can be used to generate samples from the joint posterior distribution of the model parameters, given a dataset and prior distributions. Hierarchical statistical models can be used. Optimal design of experiments can also be investigated. AVAILABILITY AND IMPLEMENTATION: The GNU GPL source is available at (http://savannah.gnu.org/projects/mcsim). A distribution package is at (http://www.gnu.org/software/mcsim). GNU MCSim is written in standard C and runs on any platform supporting a C compiler. Supplementary Material is available online at (http://www.gnu.org/software/mcsim).
Frédéric Y. Bois
Bioinform.1