biosigner: Signature discovery from omics data

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.

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 Bioconductor
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biosigner documentation built on Oct. 31, 2019, 7:26 a.m.