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/npmipd9dv6nsxnh7jksnnk1ia0n2p2ny-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/5ixs35fsfi3rs7l63498r7j4mkyi0i5s-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/21572apcp88mc0r859jhzdw7pq8kifax-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/7v71m571bhipf1b69mnf5g92acbiisj4-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/bym0bmxvgxwbnvlvpwjnsbh5qcib1plx-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/as5mmkfmsab7mjr0jik2yf6hxp0xfwpl-r-biosigner-1.16.0.drv |
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