DOI: 10.18129/B9.bioc.biosigner

Bioconductor version: Release (3.22)

Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics.

Author: Philippe Rinaudo [aut], Etienne A. Thevenot [aut, cre] ORCID: 0000-0003-1019-4577

Maintainer: Etienne A. Thevenot <etienne.thevenot at cea.fr>