DOI: 10.18129/B9.bioc.biosigner    

This package is for version 3.8 of Bioconductor; for the stable, up-to-date release version, see biosigner.

Signature discovery from omics data

Bioconductor version: 3.8

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 <phd.rinaudo at>, Etienne Thevenot <etienne.thevenot at>

Maintainer: Philippe Rinaudo <phd.rinaudo at>, Etienne Thevenot <etienne.thevenot at>

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biocViews Classification, FeatureExtraction, Lipidomics, Metabolomics, Proteomics, Software, Transcriptomics
Version 1.10.0
In Bioconductor since BioC 3.3 (R-3.3) (3 years)
License CeCILL
Imports methods, e1071, randomForest, ropls, Biobase
Suggests BioMark, RUnit, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, rmarkdown
Depends On Me
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