recipe bioconductor-enmcb

Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models

Homepage

https://bioconductor.org/packages/3.14/bioc/html/EnMCB.html

License

GPL-2

Recipe

/bioconductor-enmcb/meta.yaml

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.

package bioconductor-enmcb

Versions

1.6.0-0,  1.4.0-0,  1.2.2-0,  1.2.0-0,  1.0.2-0

Depends

• bioconductor-illuminahumanmethylation450kanno.ilmn12.hg19 >=0.6.0,<0.7.0

• bioconductor-minfi >=1.40.0,<1.41.0

• r-base >=4.1,<4.2.0a0

• r-boot

• r-doparallel

• r-foreach

• r-ggplot2

• r-glmnet

• r-mboost

• r-rms

• r-survival

• r-survivalroc

• r-survivalsvm

Required By

Installation

With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-enmcb

and update with:

conda update bioconductor-enmcb

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-enmcb:<tag>

(see bioconductor-enmcb/tags for valid values for <tag>)

Download stats

Link to this page

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