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/yrvid2r0l5l37lkb5b9cazx22vbd69mc-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/cvrlphksb7vp4b45aml9xixrlil0y3w1-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/1jzggdaddk175lj1v03w7vy2h7yhdr4y-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/97afp7ajr3k643ph7nc96fx7ld4r3ff8-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/v6lfyca8z8yzg6jhp2nr7r2y3dzr56ha-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/j8y6xif287f247ya3x2rrj9hmwqgkrmi-r-biosigner-1.16.0.drv |
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