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/wbjivxji0vy69kfz3mnm9vs21g87szph-r-biosigner-1.14.4.drv | ||
mips64el-linux | /gnu/store/ma3wyw037cvkn5w0s13jzjbjyr54mkmx-r-biosigner-1.14.4.drv | ||
i686-linux | /gnu/store/mlfk6cxsvcz2vx1g63npl6mvd5z7nb0i-r-biosigner-1.14.4.drv | ||
i586-gnu | /gnu/store/ra2w8l5v98drf64vz70x4laynghbvb5y-r-biosigner-1.14.4.drv | ||
armhf-linux | /gnu/store/byxx8zq0sh02n7jmz9wac68cfvla5v79-r-biosigner-1.14.4.drv | ||
aarch64-linux | /gnu/store/83zkhcchpyw4f0qr9djxddgc9bsj41ia-r-biosigner-1.14.4.drv |
Linter | Message | Location |
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description Validate package descriptions | use @code or similar ornament instead of quotes |