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/qw9qj8ynf1c5ms4zr69rfiilmc34ym89-r-biosigner-1.14.4.drv | ||
mips64el-linux | /gnu/store/4jyysc7cj2hx2v7kpwacx2cxxhbl69mx-r-biosigner-1.14.4.drv | ||
i686-linux | /gnu/store/mnjjjv0wk93pcdmc0c6zg40d3m3n2zfp-r-biosigner-1.14.4.drv | ||
armhf-linux | /gnu/store/yqgqdzmhbh6pdw11rns34q11fk87kpcl-r-biosigner-1.14.4.drv | ||
aarch64-linux | /gnu/store/l3vlrlxrk21dq95q2cnk6vi2x13bs3bj-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 |