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/l5qkrvjcz3zgcrskbsxqjc2ris5zzn4n-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/3pvmrj374jcxwd98n25rqyayfy6kyzrk-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/b90i9acnm2lwpdkn9cjccr372p6dr6l1-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/fczwf9dbafw3cd1h88dn1ga0ifmc7n3f-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/i901291pw65lqs5lshv1cydhiai6f2yg-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/2hijl0qjbbh19agi6xrc7x08hj3hqqjk-r-biosigner-1.16.0.drv |
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description Validate package descriptions | use @code or similar ornament instead of quotes |