sigFeature
sigFeature: Significant feature selection using SVM-RFE & t-statistic
Bioconductor version: 3.24 · Package version: 1.31.0
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.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("sigFeature") Details
| Maintainer | Pijush Das Developer <topijush@gmail.com> |
| Author | Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb] |
| License | GPL (>= 2) |
| Downloads rank | 608 |
| Source branch | devel |
| biocViews | Classification, FeatureExtraction, GeneExpression, GenePrediction, Microarray, Normalization, Software, SupportVectorMachine, Transcription, mRNAMicroarray |
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | sigFeature_1.31.0.tar.gz |
| Windows binary (x86_64) | sigFeature_1.31.0.zip |
| macOS binary (arm64) | sigFeature_1.31.0.tgz |
| macOS binary (x86_64) | sigFeature_1.31.0.tgz |
Dependencies
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