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/m3j742jgnh91b8hf01ad0vx79h4x999b-r-biosigner-1.14.4.drv | ||
mips64el-linux | /gnu/store/cm2hfp51sf56crz84fzhvvrcx64mmhvd-r-biosigner-1.14.4.drv | ||
i686-linux | /gnu/store/p7x1j66gp3gyrvbfaw0msavxdb1fjnvf-r-biosigner-1.14.4.drv | ||
armhf-linux | /gnu/store/kgw69ic0ha4jy7jx1s21l6kzd0p01cwm-r-biosigner-1.14.4.drv | ||
aarch64-linux | /gnu/store/kd0q6szfb39xzphp3y8a01c9f3iivmqi-r-biosigner-1.14.4.drv |
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