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/c1cdhmr2521ci9d2053zvgwzakp8p2dd-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/qfcgcqhgmqfc8bjpz866380dc5x48kg2-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/4gf3rs537ird0asw28ra840gxfgs0mwx-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/lar8c24c5dx374vrm6iw6pxy9bj5qra0-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/rxcqcpvmazfd3ixcmmjcka47hislixdp-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/brpqbyd2wrfrvi0pn88r320lf7hxhbkq-r-biosigner-1.16.0.drv |
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