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One tagger, many uses: Simple text-mining strategies for biomedicine

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Lars Juhl Jensen
Lars Juhl Jensen

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.

Science
1 of 70
Lars Juhl Jensen @larsjuhljensen One tagger, many uses Simple text-mining strategies for biomedicine
>10 km
too much to read
computer
as smart as a dog
teach it specific tricks
named entity recognition
dictionary
genes / proteins
chemical compounds
diseases
organisms
environments
not comprehensive
expansion rules
prefixes and suffixes
curated blacklist
SDS
software
C++ tagger
>1000 abstracts / second
inherently thread-safe
70–80% recall
80–90% precision
open source bitbucket.org/larsjuhljensen/tagger/
Python module
Docker hub.docker.com/r/larsjuhljensen/tagger/
web service tagger.jensenlab.org
Extract extract.jensenlab.org
community resources
STRING
string-db.org
functional associations
DISEASES
disease–gene associations
Cytoscape
curated knowledge
experimental data
computational predictions
co-occurrence text mining
Medline abstracts
only abstracts
<1 km
access restrictions
are abstracts sufficient?
15 million full-text articles
Westergaard et al., BioRxiv, 2017
~50% more associations
electronic health records
Jensen et al., Nature Reviews Genetics, 2012
in Danish
dictionary
drugs
adverse events
in Danish
named entity recognition
temporal correlations
Drug introduction Drug discontinuation 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
find novel associations
summary
broadly applicable
keep it simple
free tools
Acknowledgments Evangelos Pafilis Sune Pletscher- Frankild Nadezhda Doncheva Damian Szklarczyk Michael Kuhn Robert Eriksson Peter Bjødstrup Jensen John “Scooter” Morris Christian von Mering Peer Bork Christos Arvanitidis Søren Brunak
One tagger, many uses: Simple text-mining strategies for biomedicine

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