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/wfnazamnhiqgl42zblfs70al6wcn9llw-r-biosigner-1.16.0.drv | ||
mips64el-linux | /gnu/store/r0q444p9m3dn36ak2cfpk4mlsq8zmrjj-r-biosigner-1.16.0.drv | ||
i686-linux | /gnu/store/18n7fl3gzachxcbh7jh1ncx3b5ga9d0y-r-biosigner-1.16.0.drv | ||
i586-gnu | /gnu/store/k41bxsmfcnydzmhxp4y0fj61zxc0g43i-r-biosigner-1.16.0.drv | ||
armhf-linux | /gnu/store/7g2nrlzdsypjzarlvsnvq8q9gz1hqnh5-r-biosigner-1.16.0.drv | ||
aarch64-linux | /gnu/store/6d7xbsincjz54w3qj41p5bfy78pzimbk-r-biosigner-1.16.0.drv |
Linter | Message | Location |
---|---|---|
description Validate package descriptions | use @code or similar ornament instead of quotes |