EnMCB
Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models
Bioconductor version: 3.24 · Package version: 1.25.0
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 |
| Downloads rank | 456 |
| Source branch | devel |
| biocViews | DNAMethylation, MethylationArray, Normalization, Software, SupportVectorMachine |
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