Package: cocoscore
Text 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.CoCoScore 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.
In our research, we usedistant supervisionto create an automatic, but noisy, labelling of a large dataset of sentences co-mentioning two entities of interest.Free software: MIT licenseInstallationTo install CoCoScore via bioconda (for Linux and Mac OS):conda install -c bioconda cocoscoreTo install CoCoScore via pip:pip install cocoscoreCoCoScore depends onfastTextwhich needs to be installed separately if CoCoScore was installed via pip.
The installation via bioconda automatically installs fastText, too.If you installed you installed CoCoScore via pip, please build v0.1.0 of fastText as describedhereand make sure thefasttextbinary is discoverable via your$PATHenvironment variable.fastText v0.1.0 is also available viaconda-forge:conda install -c conda-forge fasttext=0.1.0CoCoScore docker container:Bioconda automatically builds a Docker container for CoCoScore.
See thepackage documentationfor more information.Quick startFollow the installation instructions above.Download thedemo.ftzfile (see next section) needed to run through the example.Run through theexampleto learn how to apply CoCoScore to your own data.Example usageBefore running the examples, please download the following file and save it todoc/example/:Example pre-trained fastText modelThe files are downloaded and placed in the correct directories by executing:wget -P doc/example/ http://download.jensenlab.org/BLAH4/demo.ftzPreprint manuscriptA preprint manuscript describing CoCoScore and its performance on eight datasets, compared to a baseline co-occurrence scoring model, is availablevia bioRxiv.Supplementary data described in the manuscript can be downloadedvia figshare.ContributorsCoCoScore is being developed by Alexander Junge and Lars Juhl Jensen at the
Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research,
Faculty of Health and Medical Sciences, University of Copenhagen, Denmark.FeedbackPlease open an issue here or write us:{alexander.junge,lars.juhl.jensen} AT cpr DOT ku DOT dkSee also:https://github.com/JungeAlexander/cocoscore/blob/master/CONTRIBUTING.rstDevelopmentTo run the all tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog1.0.0 (2010-01-25)implement new sentence score cutoff intagger.co_occurrence_score.co_occurrence_score()remove gensim depency, retain only scipy as depedencydocument/facilitate usage of different scoring modelfix issues with Travis CI runs0.2.0 (2018-11-10)Add support for Python 3.5.0.1.0 (2018-11-10)First release on PyPI.