EnMCB
Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models
Bioconductor version: 3.24 · Package version: 1.25.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
Creation of the correlated blocks using DNA methylation profiles. Machine learning models can be constructed to predict differentially methylated blocks and disease progression.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("EnMCB")Details
| Maintainer | Xin Yu <whirlsyu@gmail.com> |
| Author | Xin Yu |
| License | GPL-2 |
| Bug Reports | https://github.com/whirlsyu/EnMCB/issues |
| Source branch | devel |
| Build report | Bioconductor build system, r-universe |
| biocViews | DNAMethylation, MethylationArray, Normalization, Software, SupportVectorMachine |
| Package Short Url | https://bioconductor.org/packages/EnMCB/ |
Citation
From within R, enter citation("EnMCB"):
Xin Yu. EnMCB: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models. doi:10.18129/B9.bioc.EnMCB, R package version 1.25.0, https://bioconductor.org/packages/EnMCB.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | EnMCB_1.25.0.tar.gz |
| Windows binary (x86_64) | EnMCB_1.25.0.zip |
| macOS binary (arm64) | EnMCB_1.25.0.tgz |
| macOS binary (x86_64) | EnMCB_1.25.0.tgz |
Dependencies
Depends: R (>= 4.0)
Imports: survivalROC, glmnet, rms, mboost, Matrix, igraph, methods, survivalsvm, ggplot2, boot, e1071, survival, BiocFileCache
Suggests: SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, limma, rmarkdown