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        "author": "Alexander Junge",
        "author_email": "alexander.junge@posteo.net",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Utilities"
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        "description": "================================================================================\nCoCoScore: context-aware co-occurrence scores for text mining applications\n================================================================================\n\n\n\n.. image:: https://github.com/JungeAlexander/cocoscore/blob/master/doc/logos/CoCoScore-text-small.png\n\nText mining of the biomedical literature has been successful in retrieving interactions between proteins, non-coding RNAs, and chemicals as well as in determining tissue-specific expression and subcellular localization. Simple co-occurrence-based scoring schemes can uncover such associations by finding entity pairs that are frequently mentioned together but ignore the textual context of each co-occurrence.\n\nCoCoScore implements an improved context-aware co-occurrence scoring scheme that uses textual context to assess whether an association is described in a given sentence or not. CoCoScore achieves superior performance compared to previous approaches that rely on constant sentence scores, based on datasets of disease-gene, tissue-gene, and protein-protein associations.\nIn our research, we use `distant supervision <https://github.com/JungeAlexander/cocoscore/blob/master/doc/example/example.md#appendix-distant-supervision>`_ to create an automatic, but noisy, labelling of a large dataset of sentences co-mentioning two entities of interest.\n\nFree software: MIT license\n\n\nInstallation\n============\n\nTo install CoCoScore via bioconda (for Linux and Mac OS):\n\n\n::\n\n    conda install -c bioconda cocoscore\n\n\nTo install CoCoScore via pip:\n\n\n::\n\n    pip install cocoscore\n\n\nCoCoScore depends on `fastText <https://fasttext.cc/>`_ which needs to be installed separately if CoCoScore was installed via pip.\nThe installation via bioconda automatically installs fastText, too.\n\nIf you installed you installed CoCoScore via pip, please build v0.1.0 of fastText as described `here <https://github.com/facebookresearch/fastText/#building-fasttext-using-make-preferred>`_ and make sure the ``fasttext`` binary is discoverable via your ``$PATH`` environment variable.\n\nfastText v0.1.0 is also available via `conda-forge <https://anaconda.org/conda-forge/fasttext>`_:\n\n\n::\n\n   conda install -c conda-forge fasttext=0.1.0\n\n\n**CoCoScore docker container:**\n\n\nBioconda automatically builds a Docker container for CoCoScore. \nSee the `package documentation <https://bioconda.github.io/recipes/cocoscore/README.html>`_ for more information.\n\n\nQuick start\n===========\n\n1. Follow the installation instructions above.\n2. Download the ``demo.ftz`` file (see next section) needed to run through the example.\n3. Run through the `example <https://github.com/JungeAlexander/cocoscore/blob/master/doc/example/example.md>`_ to learn how to apply CoCoScore to your own data.\n\n\nExample usage\n==============\n\nBefore running the examples, please download the following file and save it to ``doc/example/``:\n\n- `Example pre-trained fastText model <http://download.jensenlab.org/BLAH4/demo.ftz>`_\n\nThe files are downloaded and placed in the correct directories by executing:\n\n::\n\n    wget -P doc/example/ http://download.jensenlab.org/BLAH4/demo.ftz\n\n\nPreprint manuscript\n====================\n\nA preprint manuscript describing CoCoScore and its performance on eight datasets, compared to a baseline co-occurrence scoring model, is available `via bioRxiv <https://www.biorxiv.org/content/early/2018/10/16/444398>`_.\n\nSupplementary data described in the manuscript can be downloaded `via figshare <https://doi.org/10.6084/m9.figshare.7198280.v1>`_.\n\n\nContributors\n=============\n\nCoCoScore is being developed by Alexander Junge and Lars Juhl Jensen at the\nDisease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research,\nFaculty of Health and Medical Sciences, University of Copenhagen, Denmark.\n\n\nFeedback\n===========\n\nPlease open an issue here or write us:\n``{alexander.junge,lars.juhl.jensen} AT cpr DOT ku DOT dk``\n\nSee also: https://github.com/JungeAlexander/cocoscore/blob/master/CONTRIBUTING.rst\n\n\nDevelopment\n===========\n\nTo run the all tests run::\n\n    tox\n\nNote, to combine the coverage data from all the tox environments run:\n\n.. list-table::\n    :widths: 10 90\n    :stub-columns: 1\n\n    - - Windows\n      - ::\n\n            set PYTEST_ADDOPTS=--cov-append\n            tox\n\n    - - Other\n      - ::\n\n            PYTEST_ADDOPTS=--cov-append tox\n\n\n\nChangelog\n=========\n\n1.0.0 (2010-01-25)\n------------------\n\n* implement new sentence score cutoff in ``tagger.co_occurrence_score.co_occurrence_score()``\n* remove gensim depency, retain only scipy as depedency\n* document/facilitate usage of different scoring model\n* fix issues with Travis CI runs\n\n0.2.0 (2018-11-10)\n------------------\n\n* Add support for Python 3.5.\n\n\n0.1.0 (2018-11-10)\n------------------\n\n* First release on PyPI.\n\n\n",
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