recipe bioconductor-gars

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



GPL (>= 2)



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

(downloads) docker_bioconductor-gars



depends bioconductor-damirseq:


depends bioconductor-mlseq:


depends bioconductor-summarizedexperiment:


depends r-base:


depends r-cluster:

depends r-ggplot2:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-gars

and update with::

   mamba update bioconductor-gars

To create a new environment, run:

mamba create --name myenvname bioconductor-gars

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

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

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