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/jvgi1bvjd3var9g5ki5837aw1avn926m-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/ba0yy5ks64cq8cyzlbmrkpn7h8xvdzjr-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/gmi3sljc1x0c8z2xb9g8gjckd6q0j3x9-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/ig3vmqgny52yx6gqsx47yns0b0qhnr8q-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/4897qycq57r0cwga3zb63n2n69f0f5w3-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/xddjy3fw70kjf73dzqk8zdgj7dy3sawf-r-biosigner-1.16.0.drv |
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