Mining biomedical texts
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Transcript of Mining biomedical texts
Lars Juhl Jensen
>10 km
Mining biomedical texts
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, 2009O’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
NLPNatural 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
Reflect.wsSune Frankild
Heiko HornEvangelos Pafilis
Michael KuhnReinhardt Schneider
Sean O’Donoghue
SciVerse appJuan-Carlos Silla-Castro
Sean O’Donoghue
EPJ-miningFrancisco S RoquePeter B JensenRobert ErikssonHenriette SchmockMarlene DalgaardMassimo AndreattaThomas HansenKaren SøebySøren BredkjærAnders JuulThomas WergeSøren Brunak
Thank you!
larsjuhljensen