recipe eden

The Explicit Decomposition with Neighborhoods (EDeN) is a decompositional kernel based on the Neighborhood Subgraph Pairwise Distance Kernel (NSPDK) that can be used to induce an explicit feature representation for graphs. This in turn allows the adoption of machine learning algorithm to perform supervised and unsupervised learning task in a scalable way (e.g. using fast stochastic gradient descent methods in classification and approximate neighborhood queries in clustering).






package eden

(downloads) docker_eden



depends biopython:

depends cvxopt:

depends dill:

depends esmre:

depends joblib:

depends matplotlib:

depends muscle:

depends networkx:


depends numpy:

depends openbabel:

depends pandas:

depends pybedtools:

depends python:


depends python-graphviz:

depends rdkit:

depends reportlab:

depends requests:

depends rnashapes:

depends scikit-learn:


depends scipy:


depends weblogo:



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 eden

and update with::

   mamba update eden

To create a new environment, run:

mamba create --name myenvname eden

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 `eden/tags`_ for valid values for ``<tag>``)

Download stats