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EnMCB: an R/bioconductor package for predicting disease progression based on methylation correlated blocks using ensemble models
- Source :
- Bioinformatics. 37:4282-4284
- Publication Year :
- 2021
- Publisher :
- Oxford University Press (OUP), 2021.
-
Abstract
- 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.
- Subjects :
- Statistics and Probability
0303 health sciences
biology
Computer science
Methylation
Computational biology
Biochemistry
Genome
Computer Science Applications
Support vector machine
Bioconductor
03 medical and health sciences
Computational Mathematics
0302 clinical medicine
Computational Theory and Mathematics
CpG site
Block (programming)
DNA methylation
biology.protein
Demethylase
Molecular Biology
030217 neurology & neurosurgery
030304 developmental biology
Subjects
Details
- ISSN :
- 13674811 and 13674803
- Volume :
- 37
- Database :
- OpenAIRE
- Journal :
- Bioinformatics
- Accession number :
- edsair.doi.dedup.....6dd552a6fb9c673600bbf9d78d0fa0c5