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 online infrastructure for computational metabolomics.

Versions 1.0.6, 1.1.10, 1.4.0, 1.6.0
License CeCILL
Links biotools: biosigner, doi: 10.3389/fmolb.2016.00026


With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-biosigner

and update with:

conda update bioconductor-biosigner


A Docker container is available at