Package: GARS 1.25.0

Mattia Chiesa

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

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.

Authors:Mattia Chiesa <[email protected]>, Luca Piacentini <[email protected]>

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GARS.pdf |GARS.html✨
GARS/json (API)
NEWS

# Install 'GARS' in R:

install.packages('GARS', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openjdk– OpenJDK Java runtime, using Hotspot JIT

Datasets:

GARS_Fitness_score - RNA-seq dataset for testing GARS
GARS_classes - RNA-seq dataset for testing GARS
GARS_data_norm - RNA-seq dataset for testing GARS
GARS_fit_list - RNA-seq dataset for testing GARS
GARS_pop_list - RNA-seq dataset for testing GARS
GARS_popul - RNA-seq dataset for testing GARS
GARS_res_GA - A GarsSelectedFeatures object for testing GARS

On BioConductor:GARS-1.25.0(bioc 3.20)GARS-1.24.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

13 exports 0.71 score 261 dependencies 13 mentions

Last updated 2 months agofrom:15b73554a6

Exports:AllPop FitScore GARS_create_rnd_population GARS_Crossover GARS_Elitism GARS_FitFun GARS_GA GARS_Mutation GARS_PlotFeaturesUsage GARS_PlotFitnessEvolution GARS_Selection LastPop MatrixFeatures

Dependencies:abind annotate AnnotationDbi arm aroma.light askpass backports base64enc bdsmatrix BH Biobase BiocFileCache BiocGenerics BiocIO BiocManager BiocParallel biomaRt Biostrings bit bit64 bitops blob boot brio broom bslib cachem callr car carData caret caTools checkmate class cli clock cluster coda codetools colorspace corrplot cowplot cpp11 crayon crosstalk curl DaMiRseq data.table DBI dbplyr DelayedArray deldir Deriv desc DESeq2 diagram diffobj digest doBy dplyr DT e1071 EDASeq edgeR ellipse ellipsis emmeans entropy estimability evaluate FactoMineR fansi farver fastmap filelock flashClust fontawesome foreach foreign formatR Formula fs FSelector futile.logger futile.options future future.apply genalg genefilter generics GenomeInfoDb GenomeInfoDbData GenomicAlignments GenomicFeatures GenomicRanges ggplot2 ggrepel globals glue gower gridExtra gtable hardhat highr Hmisc hms htmlTable htmltools htmlwidgets httpuv httr httr2 hwriter igraph ineq interp ipred IRanges isoband iterators jpeg jquerylib jsonlite KEGGREST KernSmooth kknn knitr labeling lambda.r later lattice latticeExtra lava lazyeval leaps lifecycle limma listenv lme4 locfit lubridate magrittr MASS Matrix MatrixGenerics MatrixModels matrixStats memoise mgcv microbenchmark mime minqa MLSeq ModelMetrics modelr multcompView munsell mvtnorm nlme nloptr nnet numDeriv openssl pamr parallelly pbkrtest pheatmap pillar pkgbuild pkgconfig pkgload plogr pls plsVarSel plyr png praise praznik prettyunits pROC processx prodlim progress progressr promises proxy ps purrr pwalign quantreg R.methodsS3 R.oo R.utils R6 randomForest rappdirs RColorBrewer Rcpp RcppArmadillo RcppEigen RCurl recipes rematch2 reshape2 restfulr Rhtslib rJava rjson rlang rmarkdown rpart rprojroot Rsamtools RSNNS RSQLite rstudioapi rtracklayer RWeka RWekajars S4Arrays S4Vectors sass scales scatterplot3d shape ShortRead snow SparseArray SparseM SQUAREM sSeq statmod stringi stringr SummarizedExperiment survival sva sys testthat tibble tidyr tidyselect timechange timeDate tinytex tzdb UCSC.utils utf8 vctrs VennDiagram viridis viridisLite waldo withr xfun XML xml2 xtable XVector yaml zlibbioc

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

Rendered fromGARS.Rnwusingknitr::knitron Jun 30 2024.

Last update: 2020-04-14
Started: 2018-02-12

Help page	Topics
Accessors for the 'AllPop' slot of a GarsSelectedFeatures object.	AllPop AllPop,GARS-AllPop AllPop,GarsSelectedFeatures-method
Accessors for the 'FitScore' slot of a GarsSelectedFeatures object.	FitScore FitScore,GARS-FitScore FitScore,GarsSelectedFeatures-method
GARS package for a robust feature selection of high-dimensional data	GARS-package GARS
RNA-seq dataset for testing GARS	GARS_classes
Create a random chromosomes population	GARS_create_rnd_population
Perform the one-point and the two-point Crossover	GARS_Crossover
RNA-seq dataset for testing GARS	GARS_data_norm
Separate chromosome on the basis of the Fitness Scores	GARS_Elitism
RNA-seq dataset for testing GARS	GARS_fit_list
This function implements the Fitness Function of GARS	GARS_FitFun
RNA-seq dataset for testing GARS	GARS_Fitness_score
The wrapper fuction to use GARS	GARS_GA
Perform the Mutation step	GARS_Mutation
A bubble chart to assess the usage of each features	GARS_PlotFeaturesUsage
Plot the maximum fitness scores for each generation	GARS_PlotFitnessEvolution
RNA-seq dataset for testing GARS	GARS_pop_list
RNA-seq dataset for testing GARS	GARS_popul
A GarsSelectedFeatures object for testing GARS	GARS_res_GA
Perform the "Roulette Wheel" or the "Tournament" selection	GARS_Selection
The output class 'GarsSelectedFeatures'	GarsSelectedFeatures-class
Accessors for the 'LastPop' slot of a GarsSelectedFeatures object.	LastPop LastPop,GARS-LastPop LastPop,GarsSelectedFeatures-method
Accessors for the 'MatrixFeatures' slot of a GarsSelectedFeatures object.	MatrixFeatures MatrixFeatures,GARS-MatrixFeatures MatrixFeatures,GarsSelectedFeatures-method

Package: GARS 1.25.0

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

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

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