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Updated: Nov 4, 2025
Comprehensive DNA Methylation Analysis Using a Methyl-CpG-binding Domain Capture-based Method in Chronic Lymphocytic Leukemia Patients
Published on: June 16, 2017
EnMCB: an R/bioconductor package for predicting disease progression based on methylation correlated blocks using
Xin Yu1,2, De-Xin Kong1,2
1State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, China.
Motivation:
Based on the concept that contiguous cytosine-phosphorothioate-guanine (CpG) sites in the same DNA strand may be modified by a methyltransferase or demethylase together, current study found that the combination of multiple CpGs into a single block may promote cancer diagnosis. However, there is no R package available for building models based on methylation correlated blocks.
Results:
Here, we present a package named stacked ensemble of machine learning models for methylation correlated blocks (EnMCB) to build signatures based on DNA methylation correlated blocks for survival prediction. The Cox regression, support vector regression, mboost and elastic-net model were combined in the ensemble model. Methylation profiles from The Cancer Genome Atlas were used as real datasets. The package automatically partitions the genome into blocks of tightly co-methylated CpG sites, termed methylation correlated blocks. After partitioning and modeling, the diagnostic capacities for predicting patients' survivals are given.
Availability And Implementation:
EnMCB is freely available for download at GitHub (https://github.com/whirlsyu/EnMCB/) and Bioconductor (http://bioconductor.org/packages/release/bioc/html/EnMCB.html).
Supplementary Information:
Supplementary data are available at Bioinformatics online.
