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/jm2dvll9czgacwrq0z4y6vf1gq3y9l2x-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/6053lkbim9f9bbw004xs8vk7ing2rmd1-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/njrb66b8isk2agxnzxnrbnfqz8mmanp4-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/q2hmz7rf0gj3hhcpak4zs2vr5ff06qn2-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/1imp3y8hhvribyivnbbjwzah55gix7cd-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/jss0kax82nqxwy1gdr7vsb83yhv2l689-r-biosigner-1.16.0.drv |
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