recipe bioconductor-gars

GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets

Homepage:

https://bioconductor.org/packages/3.16/bioc/html/GARS.html

License:

GPL (>= 2)

Recipe:

/bioconductor-gars/meta.yaml

Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets.

package bioconductor-gars

Versions:

1.18.0-0,  1.14.0-0,  1.12.0-0,  1.10.0-1,  1.10.0-0,  1.6.0-0,  1.4.0-1,  1.2.0-0

Depends:

• bioconductor-damirseq >=2.10.0,<2.11.0

• bioconductor-mlseq >=2.16.0,<2.17.0

• bioconductor-summarizedexperiment >=1.28.0,<1.29.0

• r-base >=4.2,<4.3.0a0

• r-cluster

• r-ggplot2

Required By:

Installation

With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-gars

and update with:

conda update bioconductor-gars

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-gars:<tag>

(see bioconductor-gars/tags for valid values for <tag>)

Download stats

Link to this page

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