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 |
|---|---|---|---|
| x86_64-linux | /gnu/store/41xp4mn7m7pzphfd3zszdzy0akdwjlfc-r-biosigner-1.36.0.drv | ||
| i686-linux | /gnu/store/dmy36n1ins71v8g3ccc8ds37hrz8ik5m-r-biosigner-1.36.0.drv |
| Linter | Message | Location |
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| description Validate package descriptions | use @code or similar ornament instead of quotes |
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