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Article

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

Briefings in bioinformatics - 20 Jul 2021

Deutelmoser Heike, Scherer Dominique, Brenner Hermann, Waldenberger Melanie, Suhre Karsten, Kastenmüller Gabi, Lorenzo Bermejo Justo

Abstract excerpt

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 model overfitting, the least-squares loss function of standard LASSO regression translates into a strong dependence...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Identifiers

PMID: 33063116
DOI: 10.1093/bib/bbaa230
PMCID: PMC8293825

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