Stable Iterative Variable Selection.

Pubmed ID: 34270690

Pubmed Central ID: PMC8665768

Journal: Bioinformatics (Oxford, England)

Publication Date: Dec. 11, 2021

MeSH Terms: Software, Biomarkers

Grants: 677943

Authors: Venäläinen MS, Klén R, Mahmoudian M, Elo LL

Cite As: Mahmoudian M, Venäläinen MS, Klén R, Elo LL. Stable Iterative Variable Selection. Bioinformatics 2021 Dec 11;37(24):4810-4817.

Studies:

Abstract

MOTIVATION: The emergence of datasets with tens of thousands of features, such as high-throughput omics biomedical data, highlights the importance of reducing the feature space into a distilled subset that can truly capture the signal for research and industry by aiding in finding more effective biomarkers for the question in hand. A good feature set also facilitates building robust predictive models with improved interpretability and convergence of the applied method due to the smaller feature space. RESULTS: Here, we present a robust feature selection method named Stable Iterative Variable Selection (SIVS) and assess its performance over both omics and clinical data types. As a performance assessment metric, we compared the number and goodness of the selected feature using SIVS to those selected by Least Absolute Shrinkage and Selection Operator regression. The results suggested that the feature space selected by SIVS was, on average, 41% smaller, without having a negative effect on the model performance. A similar result was observed for comparison with Boruta and caret RFE. AVAILABILITY AND IMPLEMENTATION: The method is implemented as an R package under GNU General Public License v3.0 and is accessible via Comprehensive R Archive Network (CRAN) via https://cran.r-project.org/package=sivs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.