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/z04hjw9b667fgpych0nphh9naya2vbbl-r-biosigner-1.12.0.drv | ||
i686-linux | /gnu/store/3ja0015cmkikcq8wq9jww23c1zf6pfsw-r-biosigner-1.12.0.drv |
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description Validate package descriptions | use @code or similar ornament instead of quotes |