recipe bioconductor-hpip

Host-Pathogen Interaction Prediction






HPiP (Host-Pathogen Interaction Prediction) uses an ensemble learning algorithm for prediction of host-pathogen protein-protein interactions (HP-PPIs) using structural and physicochemical descriptors computed from amino acid-composition of host and pathogen proteins.The proposed package can effectively address data shortages and data unavailability for HP-PPI network reconstructions. Moreover, establishing computational frameworks in that regard will reveal mechanistic insights into infectious diseases and suggest potential HP-PPI targets, thus narrowing down the range of possible candidates for subsequent wet-lab experimental validations.

package bioconductor-hpip

(downloads) docker_bioconductor-hpip



depends r-base:


depends r-caret:

depends r-corrplot:

depends r-dplyr:


depends r-ggplot2:

depends r-httr:


depends r-igraph:

depends r-magrittr:

depends r-mcl:

depends r-proc:

depends r-protr:

depends r-prroc:

depends r-purrr:

depends r-readr:

depends r-stringr:

depends r-tibble:

depends r-tidyr:



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-hpip

and update with::

   mamba update bioconductor-hpip

To create a new environment, run:

mamba create --name myenvname bioconductor-hpip

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

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