Mining biomedical texts

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Lars Juhl Jensen >10 km Mining biomedical texts

Transcript of Mining biomedical texts

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Lars Juhl Jensen

>10 km

Mining biomedical texts

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exponential growth

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some things are constant

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~45 seconds per paper

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information retrieval

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find the relevant texts

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still too much to read

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computer

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as smart as a dog

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teach it specific tricks

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named entity recognition

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identify the concepts

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comprehensive lexicon

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small molecules

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proteins

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cellular components

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organisms

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diseases

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orthographic variation

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“black list”

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Reflect.ws

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augmented browsing

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browser add-on

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Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009O’Donoghue et al., Journal of Web Semantics, 2010

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Firefox

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Internet Explorer

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Google Chrome

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Safari

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Utopia Documents

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web services

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~150 years of publishing

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dead wood

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dead e-wood

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added value

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collaboration

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SciVerse application

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STITCH

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Kuhn et al., Nucleic Acids Research, 2010

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curated knowledge

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drug targets

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pathways

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Letunic & Bork, Trends in Biochemical Sciences, 2008

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experimental data

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physical interactions

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Jensen & Bork, Science, 2008

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text mining

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co-mentioning

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NLPNatural Language Processing

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abstracts

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full text

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restricted access

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collaboration

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electronic patient journals

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a hard problem

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in Danish

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no lexicon

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by busy doctors

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acronyms

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typos

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about psychiatric patients

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delusions

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domain specific system

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F20

F200

Negation

Family

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diagnoses

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patient stratification

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Roque et al., PLoS Computational Biology, 2011

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disease comorbidity

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Roque et al., PLoS Computational Biology, 2011

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medication

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adverse drug events

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pharmacovigilance

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phenotype

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genotype

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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!

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