Optimized application of penalized regression methods to diverse genomic data.

ABSTRACT:

Motivation

Penalized regression methods have been adopted widely for high-dimensional feature selection and prediction in many bioinformatic and biostatistical contexts. While their theoretical properties are well-understood, specific methodology for their optimal application to genomic data has not been determined.

Results

Through simulation of contrasting scenarios of correlated high-dimensional survival data, we compared the LASSO, Ridge and Elastic Net penalties for prediction and variable selection. We found that a 2D tuning of the Elastic Net penalties was necessary to avoid mimicking the performance of LASSO or Ridge regression. Furthermore, we found that in a simulated scenario favoring the LASSO penalty, a univariate pre-filter made the Elastic Net behave more like

SUBMITTER: Waldron L 

PROVIDER: S-EPMC3232376 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature