DOI: 10.18129/B9.bioc.sigFeature  

This package is for version 3.16 of Bioconductor; for the stable, up-to-date release version, see sigFeature.

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

Bioconductor version: 3.16

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>

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


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biocViews Classification, FeatureExtraction, GeneExpression, GenePrediction, Microarray, Normalization, Software, SupportVectorMachine, Transcription, mRNAMicroarray
Version 1.16.0
In Bioconductor since BioC 3.8 (R-3.5) (4.5 years)
License GPL (>= 2)
Depends R (>= 3.5.0)
Imports biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods
Suggests RUnit, BiocGenerics, knitr, rmarkdown
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