bioconductor-biosigner¶
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/3.7/bioc/html/biosigner.html |
| Versions | 1.8.0, 1.6.0, 1.4.0, 1.1.10, 1.0.6 |
| License | CeCILL |
| Recipe | https://github.com/bioconda/bioconda-recipes/tree/master/recipes/bioconductor-biosigner |
| Links | biotools: biosigner, doi: 10.3389/fmolb.2016.00026 |
Installation¶
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 https://quay.io/repository/biocontainers/bioconductor-biosigner.
Link to this page¶
Render an 
badge with the following Markdown:
[](http://bioconda.github.io/recipes/bioconductor-biosigner/README.html)
