recipe bioconductor-gsgalgor

An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer






A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high.

package bioconductor-gsgalgor

(downloads) docker_bioconductor-gsgalgor



depends r-base:


depends r-cluster:

depends r-doparallel:

depends r-foreach:

depends r-matchingr:

depends r-nsga2r:

depends r-proxy:

depends r-survival:



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-gsgalgor

and update with::

   mamba update bioconductor-gsgalgor

To create a new environment, run:

mamba create --name myenvname bioconductor-gsgalgor

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

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