Bioconductor-mirror/biosigner: Signature discovery from omics data
Version 1.5.0

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.

Getting started

Package details

AuthorPhilippe Rinaudo <[email protected]>, Etienne Thevenot <[email protected]>
Bioconductor views Classification FeatureExtraction Lipidomics Metabolomics Proteomics Transcriptomics
MaintainerPhilippe Rinaudo <[email protected]>, Etienne Thevenot <[email protected]>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
Bioconductor-mirror/biosigner documentation built on June 1, 2017, 5:21 a.m.