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.
|Author||Philippe Rinaudo <firstname.lastname@example.org>, Etienne Thevenot <email@example.com>|
|Date of publication||None|
|Maintainer||Philippe Rinaudo <firstname.lastname@example.org>, Etienne Thevenot <email@example.com>|
biosign: Builds the molecular signature.
biosign-class: Class "biosign"
biosigner-package: Molecular signature discovery from omics data
diaplasma: Analysis of plasma from diabetic patients by LC-HRMS
getAccuracyMN: Accuracies of the full model and the models restricted to the...
getSignatureLs: Signatures selected by the models
plot: Plot method for 'biosign' signature objects
predict: Predict method for 'biosign' signature objects
show: Show method for 'biosign' signature objects