bioconductor-biosigner

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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.

Home http://bioconductor.org/packages/release/bioc/html/biosigner.html
Versions 1.0.6, 1.1.10
License CeCILL
Recipe https://github.com/bioconda/bioconda-recipes/tree/master/recipes/bioconductor-biosigner

Installation

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

conda install bioconductor-biosigner

and update with:

conda update bioconductor-biosigner

docker

A Docker container is available at https://quay.io/repository/biocontainers/bioconductor-biosigner.