GARS

DOI: 10.18129/B9.bioc.GARS    

This package is for version 3.8 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: 3.8

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 "3.5") 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	Titolo
PDF		Reference Manual
Text		NEWS

Details

biocViews	Classification, Clustering, FeatureExtraction, Software
Version	1.2.0
In Bioconductor since	BioC 3.7 (R-3.5) (1 year)
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
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Build Report

Package Archives

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

Source Package	GARS_1.2.0.tar.gz
Windows Binary	GARS_1.2.0.zip
Mac OS X 10.11 (El Capitan)	GARS_1.2.0.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	http://bioconductor.org/packages/GARS/
Package Downloads Report	Download Stats