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/388kn4jb9xgmlxmh69slh4v63jj61drj-r-biosigner-1.14.0.drv | ||
mips64el-linux | /gnu/store/sskqf764aa27zdclaajsaii72n9rq15d-r-biosigner-1.14.0.drv | ||
i686-linux | /gnu/store/x9dq8sgs8wz68bqs9dk35k5v1qy7aj0p-r-biosigner-1.14.0.drv | ||
armhf-linux | /gnu/store/kwx3hgds0sj6rv4473jav1kc5rzmnq9z-r-biosigner-1.14.0.drv | ||
aarch64-linux | /gnu/store/i3qccxqgklpw8d2p40wr7xz1mzab4awa-r-biosigner-1.14.0.drv |
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