DOI: 10.18129/B9.bioc.EnMCB    

This is the development version of EnMCB; for the stable release version, see EnMCB.

Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models

Bioconductor version: Development (3.15)

Creation of the correlated blocks using DNA methylation profiles. A stacked ensemble of machine learning models, which combined the cox, support vector machine and elastic-net regression model, can be constructed to predict disease progression.

Author: Xin Yu

Maintainer: Xin Yu <whirlsyu at gmail.com>

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biocViews DNAMethylation, MethylationArray, Normalization, Software, SupportVectorMachine
Version 1.7.1
In Bioconductor since BioC 3.11 (R-4.0) (1.5 years)
License GPL-2
Depends R (>= 4.0)
Imports foreach, doParallel, parallel, stats, survivalROC, glmnet, rms, mboost, Matrix, igraph, survivalsvm, ggplot2, IlluminaHumanMethylation450kanno.ilmn12.hg19, minfi, boot, e1071, survival, utils
Suggests SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, prognosticROC, rmarkdown
BugReports https://github.com/whirlsyu/EnMCB/issues
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