recipe bioconductor-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.

package bioconductor-enmcb

(downloads) docker_bioconductor-enmcb



depends bioconductor-biocfilecache:


depends r-base:


depends r-boot:

depends r-e1071:

depends r-ggplot2:

depends r-glmnet:

depends r-igraph:

depends r-matrix:

depends r-mboost:

depends r-rms:

depends r-survival:

depends r-survivalroc:

depends r-survivalsvm:



You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-enmcb

and update with::

   mamba update bioconductor-enmcb

To create a new environment, run:

mamba create --name myenvname bioconductor-enmcb

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull<tag>

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

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