High-dimensional supervised classification in a context of non-independence of observations to identify the determining SNPs in a phenotype

This work addresses the problem of supervised classification for highly correlated high-dimensional data describing non-independent observations to identify SNPs related to a phenotype. We use a general penalized linear mixed model with a single random effect that performs simultaneous SNP selection...

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Detalles Bibliográficos
Autores principales: Gaye, Aboubacry, Diongue, Abdou Ka, Komen, Lionel Nanguep, Diallo, Amadou, Sylla, Seydou Nourou, Diarra, Maryam, Talla, Cheikh, Loucoubar, Cheikh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505671/
https://www.ncbi.nlm.nih.gov/pubmed/37727806
http://dx.doi.org/10.1016/j.idm.2023.09.002

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10505671/
https://www.ncbi.nlm.nih.gov/pubmed/37727806
http://dx.doi.org/10.1016/j.idm.2023.09.002

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