Package: biosigner
Type: Package
Title: Signature discovery from omics data
Version: 1.10.0
Date: 2018-04-15
Author: Philippe Rinaudo <phd.rinaudo@gmail.com>, Etienne Thevenot
    <etienne.thevenot@cea.fr>
Maintainer: Philippe Rinaudo <phd.rinaudo@gmail.com>, Etienne Thevenot
 <etienne.thevenot@cea.fr>
biocViews: Classification, FeatureExtraction, Transcriptomics,
        Proteomics, Metabolomics, Lipidomics
Description: 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.
Imports: methods, e1071, randomForest, ropls, Biobase
Suggests: BioMark, RUnit, BiocGenerics, BiocStyle, golubEsets,
        hu6800.db, knitr, rmarkdown
VignetteBuilder: knitr
License: CeCILL
LazyLoad: yes
NeedsCompilation: no
Packaged: 2018-10-31 02:56:11 UTC; biocbuild
RoxygenNote: 5.0.1
git_url: https://git.bioconductor.org/packages/biosigner
git_branch: RELEASE_3_8
git_last_commit: 3146e24
git_last_commit_date: 2018-10-30
Date/Publication: 2018-10-30
Built: R 3.5.1; ; 2018-10-31 10:46:28 UTC; windows
