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/acy4nhffjifh8n1wwms2li9g9yswc2gr-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/qkf4qj9nb7a5rfx97vyw8h030fpdfqhc-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/0xmq462c7ickhpwj31gmkh1igaw1vz2i-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/80p445dkviv4p438xliq9882xj5cqjrn-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/hbhnnmv6jx107fzsdw3cjqaacn1g4021-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/qlsjixr4l1qhbffzhsiby92ym156cbwi-r-biosigner-1.16.0.drv |
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