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        "author": "Mehdi Ali",
        "author_email": "mehdi.ali@cs.uni-bonn.de",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 1 - Planning",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
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        "description": "BioKEEN |build| |coverage| |docs| |zenodo|\n==========================================\nBioKEEN (Biological KnowlEdge EmbeddiNgs) is a package for training and evaluating biological knowledge graph\nembeddings built on `PyKEEN <https://github.com/SmartDataAnalytics/PyKEEN>`_.\n\nBecause we use PyKEEN as the underlying software package, implementations of 10 knowledge graph embedding models are\ncurrently available for BioKEEN. Furthermore, BioKEEN can be run in *training mode* in which users provide their own set\nof hyper-parameter values, or in *hyper-parameter optimization mode* to find suitable hyper-parameter values from set\nof user defined values.\n\nThrough the integration of the `Bio2BEL <https://github.com/bio2bel>`_ software numerous biomedical databases are\ndirectly accessible within BioKEEN.\n\nBioKEEN can also be run without having experience in programing by using its interactive command line interface that can\nbe started with the command \u201cbiokeen\u201d from a terminal.\n\n\nNews:\n-----\n- New database integrated: DDR (disease-disease relations) Menche, J., et al. (2015)\n- All databases accessible again\n\nShare Your Experimental Artifacts\n---------------------------------\nYou can share you trained KGE models along the other experimental artifacts through the `KEEN-Model-Zoo <https://github.com/SmartDataAnalytics/KEEN-Model-Zoo>`_.\n\nTutorials\n---------\nA brief tutorial on how to get started with BioKEEN is available `here <https://vimeo.com/314252656>`_.\n\n.. image:: https://i.vimeocdn.com/video/755767182.jpg?mw=1100&mh=619&q=70\n    :width: 300px\n    :target: https://vimeo.com/314252656\n\n\n.. |br| raw:: html\n\n   <br />\n\n|br| Further tutorials are can be found in the `notebooks directory <https://github.com/SmartDataAnalytics/BioKEEN/tree/master/notebooks>`_ and in our `documentation <https://biokeen.readthedocs.io/en/latest/>`_.\n\nCitation\n--------\nIf you find BioKEEN useful in your work, please consider citing:\n\n.. [1] Ali, M., *et al.* (2019). `BioKEEN: A library for learning and evaluating biological knowledge graph embeddings\n       <https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btz117/5320556>`_. *Bioinformatics* , btz117.\n\n**Note**: ComPath has been updated, for this reason we have uploaded the dataset version that we have used for\nour experiments: `dataset <https://github.com/SmartDataAnalytics/KEEN-Model-Zoo/blob/master/bioinformatics/ComPath/compath.keen.tsv>`_\n\n\n\nInstallation |pypi_version| |python_versions| |pypi_license|\n------------------------------------------------------------\nTo install biokeen, Python 3.6+ is required, and we recommend to install it on Linux or Mac OS systems.\nPlease run following command:\n\n.. code-block:: sh\n\n    $ pip install git+https://github.com/SmartDataAnalytics/BioKEEN.git\n\nAlternatively, it can be installed from the source for development with:\n\n.. code-block:: sh\n\n    $ git clone https://github.com/SmartDataAnalytics/BioKEEN.git biokeen\n    $ cd biokeen\n    $ pip install -e .\n\nContributing\n------------\nContributions, whether filing an issue, making a pull request, or forking, are appreciated.\nSee `CONTRIBUTING.rst <https://github.com/SmartDataAnalytics/BioKEEN/blob/master/CONTRIBUTING.rst>`_ for more\ninformation on getting involved.\n\nCLI Usage\n---------\nTo show BioKEEN's available commands, please run following command:\n\n.. code-block:: sh\n\n    biokeen\n\nStarting the Training/HPO Pipeline - Set Up Your Experiment within 60 seconds\n*****************************************************************************\nTo configure an experiment via the CLI, please run following command:\n\n.. code-block:: sh\n\n    biokeen start\n\nTo start BioKEEN with an existing configuration file, please run the following command:\n\n.. code-block:: sh\n\n    biokeen start -f /path/to/config.json\n\nStarting the Prediction Pipeline\n********************************\nTo make prediction based on a trained model, please run following command:\n\n.. code-block:: sh\n\n    biokeen predict -m /path/to/model/directory -d /path/to/data/directory\n\nwhere the value for the argument **-m** is the directory containing the model, in more detail following files must be\ncontained in the directory:\n\n* configuration.json\n* entities_to_embeddings.json\n* relations_to_embeddings.json\n* trained_model.pkl\n\nThese files are created automatically created after model is trained (and evaluated) and exported in your\nspecified output directory.\n\nThe value for the argument **-d** is the directory containing the data for which inference should be applied, and it\nneeds to contain following files:\n\n* entities.tsv\n* relations.tsv\n\nwhere *entities.tsv* contains all entities of interest, and relations.tsv all relations. Both files should contain\nshould contain a single column containing all the entities/relations. Based on these files, PyKEEN will create all\ntriple permutations, and computes the predictions for them, and saves them in data directory\nin *predictions.tsv*.\n\nSummarize the Results of All Experiments\n****************************************\nTo summarize the results of all experiments, please run following command:\n\n.. code-block:: sh\n\n    biokeen summarize -d /path/to/experiments/directory -o /path/to/output/file.csv\n\nGetting Bio2BEL Data\n********************\nTo download and structure the data from a `Bio2BEL <https://github.com/bio2bel>`_ repository, run:\n\n.. code-block:: sh\n\n    biokeen data get <name>\n\nWhere ``<name>`` can be any repository name in Bio2BEL such as ``hippie``, ``mirtarbase``.\n\n.. |build| image:: https://travis-ci.org/SmartDataAnalytics/BioKEEN.svg?branch=master\n    :target: https://travis-ci.org/SmartDataAnalytics/BioKEEN\n\n.. |zenodo| image:: https://zenodo.org/badge/150270965.svg\n    :target: https://zenodo.org/badge/latestdoi/150270965\n\n.. |docs| image:: http://readthedocs.org/projects/biokeen/badge/?version=latest\n    :target: https://biokeen.readthedocs.io/en/latest/\n    :alt: Documentation Status\n\n.. |python_versions| image:: https://img.shields.io/pypi/pyversions/biokeen.svg\n    :alt: Stable Supported Python Versions\n\n.. |pypi_version| image:: https://img.shields.io/pypi/v/biokeen.svg\n    :alt: Current version on PyPI\n\n.. |pypi_license| image:: https://img.shields.io/pypi/l/biokeen.svg\n    :alt: MIT License\n\n.. |coverage| image:: https://codecov.io/gh/SmartDataAnalytics/BioKEEN/branch/master/graphs/badge.svg\n    :target: https://codecov.io/gh/SmartDataAnalytics/BioKEEN\n    :alt: Coverage Status on CodeCov\n\n\n",
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