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/vzchh4j40hng9vpchl5vdsjl5r1wz17h-r-biosigner-1.14.0.drv | ||
mips64el-linux | /gnu/store/i8p5xrbn3qmhkzjw9ykfx1bq1b6rlay2-r-biosigner-1.14.0.drv | ||
i686-linux | /gnu/store/h57zrwkmasr5baq0gkqxp9ad4aq3bcxi-r-biosigner-1.14.0.drv | ||
armhf-linux | /gnu/store/5g7hrydmbvf5cj42d2dndaksqcxjwdds-r-biosigner-1.14.0.drv | ||
aarch64-linux | /gnu/store/zhyn634qyrxd4gc173ics070w2gs9b98-r-biosigner-1.14.0.drv |
Linter | Message | Location |
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