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Signature discovery from omics data

Bioconductor version: Release (3.19)

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.

Author: Philippe Rinaudo [aut], Etienne A. Thevenot [aut, cre]

Maintainer: Etienne A. Thevenot <etienne.thevenot at>

Citation (from within R, enter citation("biosigner")):


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if (!require("BiocManager", quietly = TRUE))


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Reference Manual PDF


biocViews Classification, FeatureExtraction, Lipidomics, MassSpectrometry, Metabolomics, Proteomics, Software, Transcriptomics
Version 1.32.0
In Bioconductor since BioC 3.3 (R-3.3) (8 years)
License CeCILL
Imports Biobase, methods, e1071, grDevices, graphics, MultiAssayExperiment, MultiDataSet, randomForest, ropls, stats, SummarizedExperiment, utils
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Suggests BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat
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