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/n6cvhpknr5jbrqinmmaid9p0nsj854l0-r-biosigner-1.18.2.drv | ||
mips64el-linux | /gnu/store/sxyfdbrgqzk9sg1yfka31q2h4xn9p7m4-r-biosigner-1.18.2.drv | ||
i686-linux | /gnu/store/pgsjk87vmsr378lbql5zqpsjqg8yzrrk-r-biosigner-1.18.2.drv | ||
i586-gnu | /gnu/store/6zvlc2jfaw6z22q91z56764wjnhw69j0-r-biosigner-1.18.2.drv | ||
armhf-linux | /gnu/store/xdgs6pb8cwg9yp0hhsz439qh9k6dscn9-r-biosigner-1.18.2.drv | ||
aarch64-linux | /gnu/store/r9gyp14j3aq4w5fscvjl9dry5qkb69rf-r-biosigner-1.18.2.drv |
Linter | Message | Location |
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description Validate package descriptions | use @code or similar ornament instead of quotes |