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Mining biomedical texts Lars Juhl Jensen >10 km
exponential growth
 
 
some things are constant
 
~45 seconds per paper
information retrieval
find the relevant texts
still too much to read
computer
as smart as a dog
teach it specific tricks
 
 
named entity recognition
identify the concepts
comprehensive lexicon
small molecules
proteins
cellular components
organisms
diseases
orthographic variation
“ black list”
Reflect.ws
augmented browsing
browser add-on
Pafilis, O’Donoghue, Jensen et al.,  Nature Biotechnology , 2009 O’Donoghue et al.,  Journal of Web Semantics , 2010
Firefox
Internet Explorer
Google Chrome
Safari
Utopia Documents
web services
~150 years of publishing
 
dead wood
 
dead e-wood
added value
collaboration
 
 
SciVerse application
 
 
 
 
 
STITCH
Kuhn et al.,  Nucleic Acids Research , 2010
curated knowledge
drug targets
pathways
Letunic & Bork,  Trends in Biochemical Sciences , 2008
experimental data
physical interactions
Jensen & Bork,  Science , 2008
text mining
co-mentioning
 
NLP Natural Language Processing
 
abstracts
full text
restricted access
 
collaboration
electronic patient journals
a hard problem
in Danish
no lexicon
by busy doctors
acronyms
typos
about psychiatric patients
delusions
domain specific system
F20 F200 Negation Family
diagnoses
patient stratification
Roque et al.,  PLoS Computational Biology , 2011
disease comorbidity
Roque et al.,  PLoS Computational Biology , 2011
medication
adverse drug events
pharmacovigilance
phenotype
genotype
Thank you! ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
larsjuhljensen

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Mining biomedical texts