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/p7h2dpf3fr760ahqnvr4dr62zxdhf43y-r-biosigner-1.14.2.drv | ||
mips64el-linux | /gnu/store/4ll09d5xb1wwbbqlwf9774m97bch9lms-r-biosigner-1.14.2.drv | ||
i686-linux | /gnu/store/d33vp3avma8dq5kvsd9nljhs26l8ciga-r-biosigner-1.14.2.drv | ||
armhf-linux | /gnu/store/hyrwggkrvagkaklny0svqbmbyvg3hxj1-r-biosigner-1.14.2.drv | ||
aarch64-linux | /gnu/store/9i7a4gi1hi8fs1zki8hz91hph7h5f621-r-biosigner-1.14.2.drv |
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description Validate package descriptions | use @code or similar ornament instead of quotes |