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    "info": {
        "author": "Andre Lamurias",
        "author_email": "alamurias@lasige.di.fc.ul.pt",
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            "License :: OSI Approved :: MIT License",
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        "description": "[![Downloads](https://pepy.tech/badge/merpy)](https://pepy.tech/project/merpy)\n\nUse MER scripts inside python.\n\n\n(from the MER repository)\n\nMER is a Named-Entity Recognition tool which given any lexicon and any input text returns the list of \nterms recognized in the text, including their exact location (annotations).\n\nGiven an ontology (owl file) MER is also able to link the entities to their classes.\n\nMore information about MER can be found in:\n- MER: a Shell Script and Annotation Server for Minimal Named Entity Recognition and Linking, F. Couto and A. Lamurias, Journal of Cheminformatics, 10:58, 2018\n[https://doi.org/10.1186/s13321-018-0312-9]\n- MER: a Minimal Named-Entity Recognition Tagger and Annotation Server, F. Couto, L. Campos, and A. Lamurias, in BioCreative V.5 Challenge Evaluation, 2017\n[https://www.researchgate.net/publication/316545534_MER_a_Minimal_Named-Entity_Recognition_Tagger_and_Annotation_Server]\n\n\n**NEW**\n- Package lexicons202103.tgz is available\n- Multilingual lexicons using DeCS\n\n## Documentation\n\nhttps://merpy.readthedocs.io/en/latest/\n\n## Dependencies\n\n### awk\n\nMER was developed and tested using the GNU awk (gawk) and grep. If you have another awk interpreter in your machine, there's no assurance that the program will work.\n\nFor example, to install GNU awk on Ubuntu:\n\n```bash\nsudo apt-get install gawk\n```\n\n### ssmpy\n\nTo calculate similarities between the recognized entities\n\n```bash\npip install ssmpy\n```\n\n\n## Installation\n```bash\npip install merpy\n```\nor\n\n```bash\npython setup.py install\n```\n\nThen you might want to update the MER scripts and download preprocessed data:\n```python\n>>> import merpy\n>>> merpy.download_mer()\n>>> merpy.download_lexicons()\n```\n\n\n## Basic Usage\n\n```python\n>>> import merpy\n>>> merpy.download_lexicons()\n>>> lexicons = merpy.get_lexicons()\n>>> merpy.show_lexicons()\nlexicons preloaded:\n['lexicon', 'bireme_decs_por2020', 'bireme_decs_spa2020', 'wordnet-hyponym', 'radlex', 'doid', 'bireme_decs_eng2020', 'go', 'hp', 'chebi_lite']\nlexicons loaded ready to use:\n['bireme_decs_por2020', 'chebi_lite', 'hp', 'bireme_decs_spa2020', 'wordnet-hyponym', 'doid', 'lexicon', 'radlex', 'go', 'bireme_decs_eng2020']\nlexicons with linked concepts:\n['bireme_decs_eng2020', 'doid', 'hp', 'go', 'lexicon', 'bireme_decs_spa2020', 'bireme_decs_por2020', 'radlex', 'chebi_lite']\n\n>>> document = 'Influenza, commonly known as \"the flu\", is an infectious disease caused by an influenza virus. Symptoms can be mild to severe. The most common symptoms include: a high fever, runny nose, sore throat, muscle pains, headache, coughing, and feeling tired ... Acetylcysteine for reducing the oxygen transport and caffeine to stimulate ... fever, tachypnea ... fiebre, taquipnea ... febre, taquipneia' \n>>> entities = merpy.get_entities(document, \"hp\") # get_entities_mp uses multiprocessing (set n_cores param)\n>>> print(entities)\n[['111', '115', 'mild', 'http://purl.obolibrary.org/obo/HP_0012825'], ['119', '125', 'severe', 'http://purl.obolibrary.org/obo/HP_0012828'], ['168', '173', 'fever', 'http://purl.obolibrary.org/obo/HP_0001945'], ['181', '185', 'nose', 'http://purl.obolibrary.org/obo/UBERON_0000004'], ['200', '206', 'muscle', 'http://purl.obolibrary.org/obo/UBERON_0005090'], ['214', '222', 'headache', 'http://purl.obolibrary.org/obo/HP_0002315'], ['224', '232', 'coughing', 'http://purl.obolibrary.org/obo/HP_0012735'], ['246', '251', 'tired', 'http://purl.obolibrary.org/obo/HP_0012378'], ['288', '294', 'oxygen', 'http://purl.obolibrary.org/obo/CHEBI_15379'], ['295', '304', 'transport', 'http://purl.obolibrary.org/obo/GO_0006810'], ['335', '340', 'fever', 'http://purl.obolibrary.org/obo/HP_0001945'], ['342', '351', 'tachypnea', 'http://purl.obolibrary.org/obo/HP_0002789'], ['175', '185', 'runny nose', 'http://purl.obolibrary.org/obo/HP_0031417'], ['187', '198', 'sore throat', 'http://purl.obolibrary.org/obo/HP_0033050']]\n\n>>> entities = merpy.get_entities(document, \"bireme_decs_por2020\") \n>>> print(entities)\n[['0', '9', 'Influenza', 'https://decs.bvsalud.org/ths/?filter=ths_regid&q=D007251'], ['78', '87', 'influenza', 'https://decs.bvsalud.org/ths/?filter=ths_regid&q=D007251'], ['378', '383', 'febre', 'https://decs.bvsalud.org/ths/?filter=ths_regid&q=D005334'], ['385', '395', 'taquipneia', 'https://decs.bvsalud.org/ths/?filter=ths_regid&q=D059246']]\n\n>>> merpy.create_lexicon([\"gene1\", \"gene2\", \"gene3\"], \"genelist\")\nwrote genelist lexicon\n>>> merpy.process_lexicon(\"genelist\")\n>>> merpy.delete_lexicon(\"genelist\")\ndeleted genelist lexicon\n>>> merpy.download_lexicon(\"https://github.com/lasigeBioTM/MER/raw/biocreative2017/data/ChEBI.txt\", \"chebi\")\nwrote chebi lexicon\n>>> merpy.process_lexicon(\"chebi\")\n```\n\n## Semantic Similarities \n\n```bash\nwget http://labs.rd.ciencias.ulisboa.pt/dishin/chebi202104.db.gz\ngunzip -N chebi202104.db.gz\n```\n\n```python\n>>> import merpy\n>>> merpy.process_lexicon(\"lexicon\")\n>>> document = \"\u03b1-maltose and nicotinic acid was found, but not nicotinic acid D-ribonucleotide\"\n>>> entities = merpy.get_entities(document, \"lexicon\") \n>>> merpy.get_similarities(entities, 'chebi.db')\n[['0', '9', '\u03b1-maltose', 'http://purl.obolibrary.org/obo/CHEBI_18167', 0.02834388514184269], ['14', '28', 'nicotinic acid', 'http://purl.obolibrary.org/obo/CHEBI_15940', 0.07402224403263755], ['48', '62', 'nicotinic acid', 'http://purl.obolibrary.org/obo/CHEBI_15940', 0.07402224403263755], ['48', '79', 'nicotinic acid D-ribonucleotide', 'http://purl.obolibrary.org/obo/CHEBI_15763', 0.07402224403263755]]\n\n```\n\n\n",
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