recipe bioconductor-cosnet

Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/COSNet.html

License:

GPL (>= 2)

Recipe:

/bioconductor-cosnet/meta.yaml

Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted.

package bioconductor-cosnet

(downloads) docker_bioconductor-cosnet

versions:
1.36.0-01.34.0-01.32.0-21.32.0-11.32.0-01.28.0-21.28.0-11.28.0-01.26.0-0

1.36.0-01.34.0-01.32.0-21.32.0-11.32.0-01.28.0-21.28.0-11.28.0-01.26.0-01.24.0-11.24.0-01.22.0-01.20.0-01.18.0-11.18.0-01.16.0-0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc-ng:

>=12

depends liblapack:

>=3.9.0,<4.0a0

depends r-base:

>=4.3,<4.4.0a0

requirements:

Installation

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-cosnet

and update with::

   mamba update bioconductor-cosnet

To create a new environment, run:

mamba create --name myenvname bioconductor-cosnet

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 quay.io/biocontainers/bioconductor-cosnet:<tag>

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

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