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/dd00xskrp0c50yn9077p77aqbqa2x1a7-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/qc2qvk7z4kk2m4cmjw4l1qwvi6dldnm5-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/3nkvyfyj4rjjb4yld2qg5yw4g1ind6ga-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/ddgfhcs8k5fd6125pgbanf094l2ad9ds-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/ffxfh2c8m2b2iypvbha4j72f5064jsp9-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/lh27jc52i8bnh9ixj2lmj74qbhsqga5p-r-biosigner-1.16.0.drv |
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