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/ywgh3ixcrb57blg2imvprq4jghlbm699-r-biosigner-1.18.2.drv | ||
mips64el-linux | /gnu/store/fij4bdxjbv3qx9anz3icnnhx3la908w7-r-biosigner-1.18.2.drv | ||
i686-linux | /gnu/store/0ix4sl4pp2ddgcl93wz142sryvdmya44-r-biosigner-1.18.2.drv | ||
i586-gnu | /gnu/store/hffm3v3r0d49lgvj142simy03kf0ikma-r-biosigner-1.18.2.drv | ||
armhf-linux | /gnu/store/jgmb3szzcv5mbaz3k23vn4ghrfb7d4xs-r-biosigner-1.18.2.drv | ||
aarch64-linux | /gnu/store/5bf32lbqm6bml2jh5lx30b411mha0xly-r-biosigner-1.18.2.drv |
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