Robust Huber-LASSO for improved prediction of protein, metabolite and gene expression levels relying on individual genotype data

Least absolute shrinkage and selection operator (LASSO) regression is often applied to select the most promising set of single nucleotide polymorphisms (SNPs) associated with a molecular phenotype of interest. While the penalization parameter λ restricts the number of selected SNPs and the potential...

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Detalles Bibliográficos
Autores principales: Deutelmoser, Heike, Scherer, Dominique, Brenner, Hermann, Waldenberger, Melanie, Suhre, Karsten, Kastenmüller, Gabi, Lorenzo Bermejo, Justo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Method Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293825/
https://www.ncbi.nlm.nih.gov/pubmed/33063116
http://dx.doi.org/10.1093/bib/bbaa230

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8293825/
https://www.ncbi.nlm.nih.gov/pubmed/33063116
http://dx.doi.org/10.1093/bib/bbaa230

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