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Selecting predictive biomarkers from genomic data

Frommlet, Florian ; Szulc, Piotr ; König, Franz ; Bogdan, Malgorzata Rapallo, Fabio

PloS one, 2022-06, Vol.17 (6), p.e0269369-e0269369 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Selecting predictive biomarkers from genomic data
Autor: Frommlet, Florian ; Szulc, Piotr ; König, Franz ; Bogdan, Malgorzata
Rapallo, Fabio
Assuntos: Annan medicin och hälsovetenskap ; Bayesian analysis ; Bayesian information criterion ; Biology and Life Sciences ; Biomarkers ; Clinical trials ; Feature selection ; Fines & penalties ; Genetic markers ; Machine learning ; Mathematical models ; Medical and Health Sciences ; Medical genetics ; Medical research ; Medicin och hälsovetenskap ; Medicine, Experimental ; Nucleotides ; Other Medical Sciences ; Other Medical Sciences not elsewhere specified ; Performance prediction ; Physical Sciences ; Regression analysis ; Research and Analysis Methods ; Single nucleotide polymorphisms ; Single-nucleotide polymorphism ; Sparsity ; Subgroups ; Testing procedures ; Övrig annan medicin och hälsovetenskap
É parte de: PloS one, 2022-06, Vol.17 (6), p.e0269369-e0269369
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Competing Interests: The authors have declared that no competing interests exist.
Descrição: Recently there have been tremendous efforts to develop statistical procedures which allow to determine subgroups of patients for which certain treatments are effective. This article focuses on the selection of prognostic and predictive genetic biomarkers based on a relatively large number of candidate Single Nucleotide Polymorphisms (SNPs). We consider models which include prognostic markers as main effects and predictive markers as interaction effects with treatment. We compare different high-dimensional selection approaches including adaptive lasso, a Bayesian adaptive version of the Sorted L-One Penalized Estimator (SLOBE) and a modified version of the Bayesian Information Criterion (mBIC2). These are compared with classical multiple testing procedures for individual markers. Having identified predictive markers we consider several different approaches how to specify subgroups susceptible to treatment. Our main conclusion is that selection based on mBIC2 and SLOBE has similar predictive performance as the adaptive lasso while including substantially fewer biomarkers.
Editor: United States: Public Library of Science
Idioma: Inglês

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Selecting predictive biomarkers from genomic data

Frommlet, Florian ; Szulc, Piotr ; König, Franz ; Bogdan, Malgorzata Rapallo, Fabio

United States: Public Library of Science

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