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 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.
System | Target | Derivation | Build status |
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x86_64-linux | /gnu/store/lgfz14qvnr7ajnqimqlx4ga4k225w66i-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/bghpsw0d4h5yglq0094ywf5jakcjpq4l-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/5kfg02mj1j2mir5zcnzrfb46q40ji4kl-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/qwp14jyncfmday7g95pnv299shp84864-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/w1ckdallnnmmakc9az7qj8w1a8fggpf1-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/fiyz2a7gi0z2rvfar8w21bmjx538skpj-r-biosigner-1.16.0.drv |
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