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 <[email protected]>, Etienne Thevenot <[email protected]>|
|Bioconductor views||Classification FeatureExtraction Lipidomics Metabolomics Proteomics Transcriptomics|
|Maintainer||Philippe Rinaudo <[email protected]>, Etienne Thevenot <[email protected]>|
|Package repository||View on GitHub|
Install the latest version of this package by entering the following in R:
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.