Package: EnMCB 1.24.0

Xin Yu

EnMCB: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models

Creation of the correlated blocks using DNA methylation profiles. Machine learning models can be constructed to predict differentially methylated blocks and disease progression.

Authors:Xin Yu

EnMCB_1.24.0.tar.gz
EnMCB_1.24.0.zip(r-4.7)EnMCB_1.24.0.zip(r-4.6)EnMCB_1.24.0.zip(r-4.5)
EnMCB_1.24.0.tgz(r-4.6-any)EnMCB_1.24.0.tgz(r-4.5-any)
EnMCB_1.24.0.tar.gz(r-4.7-any)EnMCB_1.24.0.tar.gz(r-4.6-any)
EnMCB_1.24.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
EnMCB/json (API)
NEWS

# Install 'EnMCB' in R:

install.packages('EnMCB', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/whirlsyu/enmcb/issues

Datasets:

anno_matrix - IlluminaHumanMethylation450kanno
demo_data - Expression matrix of demo dataset.
demo_MCBinformation - MCB information.
demo_survival_data - Survival data of demo dataset.

On BioConductor:EnMCB-1.25.0(bioc 3.24)EnMCB-1.24.0(bioc 3.23)

normalization dnamethylation methylationarray supportvectormachine

5.26 score 9 stars 4 scripts 358 downloads 15 exports 115 dependencies

Last updated from:d410f9e905 (on RELEASE_3_23). Checks:1 ERROR, 9 OK. Indexed: no.

Target	Result	Time
bioc-checks	ERROR	255
linux-devel-x86_64	OK	347
source / vignettes	OK	381
linux-release-x86_64	OK	387
macos-release-arm64	OK	266
macos-oldrel-arm64	OK	309
windows-devel	OK	257
windows-release	OK	227
windows-oldrel	OK	233
wasm-release	OK	224

Exports:as.ridgemat CompareMCB create_demo DiffMCB draw_survival_curve ensemble_model ensemble_prediction fast_roc_calculation IdentifyMCB IdentifyMCB_parallel metricMCB metricMCB.cv multi_coxph pre_process_methylation univ_coxph

Dependencies:askpass backports base64enc BiocFileCache bit bit64 blob boot bslib cachem checkmate class cli cluster codetools colorspace cpp11 curl data.table DBI dbplyr digest dplyr e1071 evaluate farver fastmap filelock fontawesome foreach foreign Formula fs generics ggplot2 glmnet glue gridExtra gtable highr Hmisc htmlTable htmltools htmlwidgets httr2 igraph inum isoband iterators jquerylib jsonlite kernlab knitr labeling lattice libcoin lifecycle magrittr MASS Matrix MatrixModels mboost memoise mime multcomp mvtnorm nlme nnet nnls openssl partykit pillar pkgconfig polspline pracma proxy purrr quadprog quantreg R6 rappdirs RColorBrewer Rcpp RcppEigen rlang rmarkdown rms rpart RSQLite rstudioapi S7 sandwich sass scales shape SparseM stabs stringi stringr survival survivalROC survivalsvm sys TH.data tibble tidyr tidyselect tinytex utf8 vctrs viridisLite withr xfun yaml zoo

EnMCB

Xin Yu

Rendered fromvignette.Rmdusingknitr::rmarkdownon May 29 2026.

Last update: 2022-06-02
Started: 2019-12-19

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
IlluminaHumanMethylation450kanno	anno_matrix
data frame ridge matrix	as.data.frame.ridgemat
ridge matrix	as.ridgemat
Compare multiple methylation correlated blocks lists	CompareMCB
create demo matrix	create_demo
Expression matrix of demo dataset.	demo_data
MCB information.	demo_MCBinformation
Survival data of demo dataset.	demo_survival_data
Differential expressed methylation correlated blocks	DiffMCB
draw survival curve	draw_survival_curve
Trainging stacking ensemble model for Methylation Correlation Block	ensemble_model
fitting function using stacking ensemble model for Methylation Correlation Block	ensemble_prediction
Fast calculation of AUC for ROC using parallel strategy	fast_roc_calculation
Identification of methylation correlated blocks	IdentifyMCB
Identification of methylation correlated blocks with parallel algorithm	IdentifyMCB_parallel
Calculation of the metric matrix for Methylation Correlation Block	metricMCB
Calculation of model AUC for Methylation Correlation Blocks using cross validation	metricMCB.cv
multivariate survival analysis using coxph	multi_coxph
Preprocess the Beta value matrix	pre_process_methylation
predict coxph penal using MCB	predict.mcb.coxph.penal
univariate and multivariate survival analysis using coxph	univ_coxph