XAItest
XAItest: Enhancing Feature Discovery with eXplainable AI
Bioconductor version: 3.23 · Package version: 1.4.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
XAItest is an R Package that identifies features using eXplainable AI (XAI) methods such as SHAP or LIME. This package allows users to compare these methods with traditional statistical tests like t-tests, empirical Bayes, and Fisher's test. Additionally, it includes simThresh, a system that enables the comparison of feature importance with p-values by incorporating calibrated simulated data.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("XAItest")Details
| Maintainer | Ghislain FIEVET <ghislain.fievet@gmail.com> |
| Author | Ghislain FIEVET [aut, cre] (ORCID: <https://orcid.org/0000-0002-0337-7327>), Sébastien HERGALANT [aut] (ORCID: <https://orcid.org/0000-0001-8456-7992>) |
| License | MIT + file LICENSE |
| URL | https://github.com/GhislainFievet/XAItest |
| Bug Reports | https://github.com/GhislainFievet/XAItest/issues |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | Classification, FeatureExtraction, Regression, Software, StatisticalMethod |
| Package Short Url | https://bioconductor.org/packages/XAItest/ |
Citation
From within R, enter citation("XAItest"):
Ghislain FIEVET, Sébastien HERGALANT. XAItest: XAItest: Enhancing Feature Discovery with eXplainable AI. doi:10.18129/B9.bioc.XAItest, R package version 1.4.0, https://bioconductor.org/packages/XAItest.
Generated from the package metadata; it may differ from the package's own citation.
Download
Follow the installation instructions to use this package in your R session.
| Source package | XAItest_1.4.0.tar.gz |
| Windows binary (x86_64) | XAItest_1.3.2.zip |
| macOS binary (arm64) | XAItest_1.4.0.tgz |
| macOS binary (x86_64) | XAItest_1.4.0.tgz |
Dependencies
Depends: R (>= 3.5.0)
Imports: limma, randomForest, kernelshap, caret, lime, DT, methods, SummarizedExperiment, ggplot2
Suggests: knitr, ggforce, shapr (>= 1.0.1), airway, xgboost, BiocGenerics, RUnit, S4Vectors