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Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models

BACKGROUND: The standard lasso penalty and its extensions are commonly used to develop a regularized regression model while selecting candidate predictor variables on a time-to-event outcome in high-dimensional data. However, these selection methods focus on a homogeneous set of variables and do not...

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Detalhes bibliográficos
Publicado no:BMC Bioinformatics
Main Authors: Belhechmi, Shaima, Bin, Riccardo De, Rotolo, Federico, Michiels, Stefan
Formato: Artigo
Idioma:Inglês
Publicado em: BioMed Central 2020
Assuntos:
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7331150/
https://ncbi.nlm.nih.gov/pubmed/32615919
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/s12859-020-03618-y
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