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GARS

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

Bioconductor version: Release (3.17)

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.

Author: Mattia Chiesa <mattia.chiesa at hotmail.it>, Luca Piacentini <luca.piacentini at cardiologicomonzino.it>

Maintainer: Mattia Chiesa <mattia.chiesa at hotmail.it>

Citation (from within R, enter citation("GARS")):

Installation

To install this package, start R (version "4.3") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GARS")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("GARS")
GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets PDF R Script
Reference Manual PDF
NEWS Text

Details

biocViews Classification, Clustering, FeatureExtraction, Software
Version 1.20.0
In Bioconductor since BioC 3.7 (R-3.5) (5.5 years)
License GPL (>= 2)
Depends R (>= 3.5), ggplot2, cluster
Imports DaMiRseq, MLSeq, stats, methods, SummarizedExperiment
Linking To
Suggests BiocStyle, knitr, testthat
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package GARS_1.20.0.tar.gz
Windows Binary GARS_1.20.0.zip (64-bit only)
macOS Binary (x86_64) GARS_1.20.0.tgz
macOS Binary (arm64) GARS_1.20.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/GARS
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/GARS
Bioc Package Browser https://code.bioconductor.org/browse/GARS/
Package Short Url https://bioconductor.org/packages/GARS/
Package Downloads Report Download Stats