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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...
| Autores principales: | , , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
KeAi Publishing
2023
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| Materias: | |
| 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 |