Journal
BIOINFORMATICS
Volume 27, Issue 24, Pages 3399-3406Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btr591
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Funding
- National Science Foundation [NSF DBI-1053486]
- Canada Foundation for Innovation (CFI) [12301, 203383]
- Ontario Research Fund [GL2-01-030]
- Canada Research Chair Program
- Ontario Ministry of Health and Long Term Care
- Div Of Biological Infrastructure
- Direct For Biological Sciences [1053486] Funding Source: National Science Foundation
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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 Ridge regression, which was detrimental to prediction performance. We demonstrate the real-life application of these methods to predicting the survival of cancer patients from microarray data, and to classification of obese and lean individuals from metagenomic data. Based on these results, we provide an optimized set of guidelines for the application of penalized regression for reproducible class comparison and prediction with genomic data.
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