Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest

Simulation‐based methods such as approximate Bayesian computation (ABC) are well‐adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inference...

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Detalles Bibliográficos
Autores principales: Collin, François‐David, Durif, Ghislain, Raynal, Louis, Lombaert, Eric, Gautier, Mathieu, Vitalis, Renaud, Marin, Jean‐Michel, Estoup, Arnaud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
RESOURCE ARTICLES
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596733/
https://www.ncbi.nlm.nih.gov/pubmed/33950563
http://dx.doi.org/10.1111/1755-0998.13413

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8596733/
https://www.ncbi.nlm.nih.gov/pubmed/33950563
http://dx.doi.org/10.1111/1755-0998.13413

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