recipe bioconductor-lpnet

Linear Programming Model for Network Inference



Artistic License 2.0




biotools: lpnet, doi: 10.1093/bioinformatics/btv327

lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used.

package bioconductor-lpnet

(downloads) docker_bioconductor-lpnet



depends r-base:


depends r-lpsolve:



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-lpnet

and update with::

   mamba update bioconductor-lpnet

To create a new environment, run:

mamba create --name myenvname bioconductor-lpnet

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

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