recipe bioconductor-biomm

BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data






The identification of reproducible biological patterns from high-dimensional omics data is a key factor in understanding the biology of complex disease or traits. Incorporating prior biological knowledge into machine learning is an important step in advancing such research. We have proposed a biologically informed multi-stage machine learing framework termed BioMM specifically for phenotype prediction based on omics-scale data where we can evaluate different machine learning models with prior biological meta information.

package bioconductor-biomm

(downloads) docker_bioconductor-biomm



depends bioconductor-biocparallel:


depends bioconductor-topgo:


depends r-base:


depends r-cmplot:

depends r-e1071:

depends r-ggplot2:

depends r-glmnet:

depends r-imager:

depends r-lattice:

depends r-nsprcomp:

depends r-precrec:

depends r-ranger:

depends r-rms:

depends r-vioplot:

depends r-xlsx:



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-biomm

and update with::

   mamba update bioconductor-biomm

To create a new environment, run:

mamba create --name myenvname bioconductor-biomm

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-biomm/tags`_ for valid values for ``<tag>``)

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