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/vqb4lfw5ylhgq83d72nkbdb9na538j5i-r-biosigner-1.14.0.drv | ||
mips64el-linux | /gnu/store/lp3faa81f614mmjr421b67gsyjqdnryl-r-biosigner-1.14.0.drv | ||
i686-linux | /gnu/store/ka8cj5849hqp9l65kpjgg6sx8ma54il9-r-biosigner-1.14.0.drv | ||
armhf-linux | /gnu/store/32a1wzzg27il0dsrim0x0w7xxb96bfhh-r-biosigner-1.14.0.drv | ||
aarch64-linux | /gnu/store/qwf1z3h0159r4kj421m09q2i612r0kd3-r-biosigner-1.14.0.drv |
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