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One tagger, many uses: Simple text-mining strategies for biomedicine
The document summarizes a text mining tool called a tagger that can be used for named entity recognition in biomedical texts. It recognizes genes, proteins, chemicals, diseases, and other entities. The tagger is open source, runs quickly at over 1000 abstracts per second, and has 70-80% recall and 80-90% precision. It comes with Python and Docker implementations and can be accessed via a web service. It is useful for tasks like extracting functional associations from literature and electronic health records.
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One tagger, many uses: Simple text-mining strategies for biomedicine
- 1. Lars Juhl Jensen @larsjuhljensen Onetagger, many uses Simple text-mining strategies for biomedicine
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- 50. 15 million full-textarticles
- 51. Westergaard et al.,BioRxiv, 2017
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- 54. Jensen et al.,Nature Reviews Genetics, 2012
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- 63. Drug introduction Drugdiscontinuation Adverse eventNegative modifier Indication Pre-existing condition Adverse drug reaction Possible adverse drug reaction Adverse event ADR of additional drug Identification start Eriksson et al., Drug Safety, 2014
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- 69. Acknowledgments Evangelos Pafilis Sune Pletscher- Frankild Nadezhda Doncheva DamianSzklarczyk Michael Kuhn Robert Eriksson Peter Bjødstrup Jensen John “Scooter” Morris Christian von Mering Peer Bork Christos Arvanitidis Søren Brunak