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

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

Abstract 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 an...

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Bibliographic Details
Main Authors: Shaima Belhechmi, Riccardo De Bin, Federico Rotolo, Stefan Michiels
Format: Article
Language:English
Published: BMC 2020-07-01
Series:BMC Bioinformatics
Subjects:
Lasso penalty
High-dimensional
Biomarker selection
Pathways
Cox model
Precision medicine
Online Access:http://link.springer.com/article/10.1186/s12859-020-03618-y

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http://link.springer.com/article/10.1186/s12859-020-03618-y

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