This is the released version of sigFeature; for the devel version, see sigFeature.

sigFeature: Significant feature selection using SVM-RFE & t-statistic


Bioconductor version: Release (3.22)

This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy.

Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb]

Maintainer: Pijush Das Developer <topijush at gmail.com>

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

Installation

To install this package, start R (version "4.5") and enter: 


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

BiocManager::install("sigFeature")

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("sigFeature")

Details

biocViews Classification, FeatureExtraction, GeneExpression, GenePrediction, Microarray, Normalization, Software, SupportVectorMachine, Transcription, mRNAMicroarray
Version 1.28.0
In Bioconductor since BioC 3.8 (R-3.5) (7.5 years)
License GPL (>= 2)
Depends R (>= 3.5.0)
Imports biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods
System Requirements
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Follow Installation instructions to use this package in your R session.