biosigner: Signature discovery from omics data
Version 1.4.0

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 Workflow4metabolomics.org online infrastructure for computational metabolomics.

Package details

AuthorPhilippe Rinaudo <phd.rinaudo@gmail.com>, Etienne Thevenot <etienne.thevenot@cea.fr>
Bioconductor views Classification FeatureExtraction Lipidomics Metabolomics Proteomics Transcriptomics
MaintainerPhilippe Rinaudo <phd.rinaudo@gmail.com>, Etienne Thevenot <etienne.thevenot@cea.fr>
LicenseCeCILL
Version1.4.0
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("biosigner")

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biosigner documentation built on May 31, 2017, 3:43 p.m.