GARS

DOI: 10.18129/B9.bioc.GARS    

This package is for version 3.13 of Bioconductor; for the stable, up-to-date release version, see GARS.

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

Bioconductor version: 3.13

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>

Installation

To install this package, start R (version "4.1") and enter:

if (!requireNamespace("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")

 

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

Details

biocViews	Classification, Clustering, FeatureExtraction, Software
Version	1.12.0
In Bioconductor since	BioC 3.7 (R-3.5) (3.5 years)
License	GPL (>= 2)
Depends	R (>= 3.5), ggplot2, cluster
Imports	DaMiRseq, MLSeq, stats, methods, SummarizedExperiment
LinkingTo
Suggests	BiocStyle, knitr, testthat
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Links To Me
Build Report

Package Archives

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

Source Package	GARS_1.12.0.tar.gz
Windows Binary	GARS_1.12.0.zip
macOS 10.13 (High Sierra)	GARS_1.12.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/GARS
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/GARS
Package Short Url	https://bioconductor.org/packages/GARS/
Package Downloads Report	Download Stats