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Bioconductor version: Release (3.5)
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 <phd.rinaudo at gmail.com>, Etienne Thevenot <etienne.thevenot at cea.fr>
Maintainer: Philippe Rinaudo <phd.rinaudo at gmail.com>, Etienne Thevenot <etienne.thevenot at cea.fr>
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