recipe bioconductor-mapredictdsc

Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge







biotools: mapredictdsc, doi: 10.1093/bioinformatics/btt492

This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types.

package bioconductor-mapredictdsc

(downloads) docker_bioconductor-mapredictdsc



depends bioconductor-affy:


depends bioconductor-annotationdbi:


depends bioconductor-gcrma:


depends bioconductor-hgu133plus2.db:


depends bioconductor-limma:


depends bioconductor-lungcanceracvssccgeo:


depends bioconductor-roc:


depends r-base:


depends r-caret:

depends r-class:

depends r-e1071:

depends r-mass:

depends r-rocr:



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-mapredictdsc

and update with::

   mamba update bioconductor-mapredictdsc

To create a new environment, run:

mamba create --name myenvname bioconductor-mapredictdsc

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

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