Using ontology-based annotation to profile disease research
Identifieur interne : 000134 ( Ncbi/Merge ); précédent : 000133; suivant : 000135Using ontology-based annotation to profile disease research
Auteurs : Yi Liu [États-Unis] ; Adrien Coulet [États-Unis, France] ; Paea Lependu [États-Unis] ; Nigam H. Shah [États-Unis]Source :
- Journal of the American Medical Informatics Association : JAMIA [ 1067-5027 ] ; 2012.
Abstract
Profiling the allocation and trend of research activity is of interest to funding agencies, administrators, and researchers. However, the lack of a common classification system hinders the comprehensive and systematic profiling of research activities. This study introduces ontology-based annotation as a method to overcome this difficulty. Analyzing over a decade of funding data and publication data, the trends of disease research are profiled across topics, across institutions, and over time.
This study introduces and explores the notions of research sponsorship and allocation and shows that leaders of research activity can be identified within specific disease areas of interest, such as those with high mortality or high sponsorship. The funding profiles of disease topics readily cluster themselves in agreement with the ontology hierarchy and closely mirror the funding agency priorities. Finally, four temporal trends are identified among research topics.
This work utilizes disease ontology (DO)-based annotation to profile effectively the landscape of biomedical research activity. By using DO in this manner a use-case driven mechanism is also proposed to evaluate the utility of classification hierarchies.
Url:
DOI: 10.1136/amiajnl-2011-000631
PubMed: 22494789
PubMed Central: 3392849
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Shah</name><affiliation wicri:level="2"><nlm:aff id="aff1">Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">22494789</idno><idno type="pmc">3392849</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3392849</idno><idno type="RBID">PMC:3392849</idno><idno type="doi">10.1136/amiajnl-2011-000631</idno><date when="2012">2012</date><idno type="wicri:Area/Pmc/Corpus">000059</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000059</idno><idno type="wicri:Area/Pmc/Curation">000059</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Curation">000059</idno><idno type="wicri:Area/Pmc/Checkpoint">000045</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Checkpoint">000045</idno><idno type="wicri:Area/Ncbi/Merge">000134</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Using ontology-based annotation to profile disease research</title><author><name sortKey="Liu, Yi" sort="Liu, Yi" uniqKey="Liu Y" first="Yi" last="Liu">Yi Liu</name><affiliation wicri:level="2"><nlm:aff id="aff1">Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Coulet, Adrien" sort="Coulet, Adrien" uniqKey="Coulet A" first="Adrien" last="Coulet">Adrien Coulet</name><affiliation wicri:level="2"><nlm:aff id="aff1">Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation><affiliation wicri:level="1"><nlm:aff id="aff2">LORIA–INRIA Nancy, Vandoeuvre-lès-Nancy Cedex, France</nlm:aff><country xml:lang="fr">France</country><wicri:regionArea>LORIA–INRIA Nancy, Vandoeuvre-lès-Nancy Cedex</wicri:regionArea><wicri:noRegion>Vandoeuvre-lès-Nancy Cedex</wicri:noRegion><wicri:noRegion>Vandoeuvre-lès-Nancy Cedex</wicri:noRegion></affiliation></author><author><name sortKey="Lependu, Paea" sort="Lependu, Paea" uniqKey="Lependu P" first="Paea" last="Lependu">Paea Lependu</name><affiliation wicri:level="2"><nlm:aff id="aff1">Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author><author><name sortKey="Shah, Nigam H" sort="Shah, Nigam H" uniqKey="Shah N" first="Nigam H" last="Shah">Nigam H. Shah</name><affiliation wicri:level="2"><nlm:aff id="aff1">Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California</wicri:regionArea><placeName><region type="state">Californie</region></placeName></affiliation></author></analytic><series><title level="j">Journal of the American Medical Informatics Association : JAMIA</title><idno type="ISSN">1067-5027</idno><idno type="eISSN">1527-974X</idno><imprint><date when="2012">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Profiling the allocation and trend of research activity is of interest to funding agencies, administrators, and researchers. However, the lack of a common classification system hinders the comprehensive and systematic profiling of research activities. This study introduces ontology-based annotation as a method to overcome this difficulty. Analyzing over a decade of funding data and publication data, the trends of disease research are profiled across topics, across institutions, and over time.</p></sec><sec><title>Results</title><p>This study introduces and explores the notions of research sponsorship and allocation and shows that leaders of research activity can be identified within specific disease areas of interest, such as those with high mortality or high sponsorship. The funding profiles of disease topics readily cluster themselves in agreement with the ontology hierarchy and closely mirror the funding agency priorities. Finally, four temporal trends are identified among research topics.</p></sec><sec><title>Conclusions</title><p>This work utilizes disease ontology (DO)-based annotation to profile effectively the landscape of biomedical research activity. By using DO in this manner a use-case driven mechanism is also proposed to evaluate the utility of classification hierarchies.</p></sec></div></front></TEI><pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">J Am Med Inform Assoc</journal-id><journal-id journal-id-type="iso-abbrev">J Am Med Inform Assoc</journal-id><journal-id journal-id-type="publisher-id">jamia</journal-id><journal-id journal-id-type="hwp">amiajnl</journal-id><journal-title-group><journal-title>Journal of the American Medical Informatics Association : JAMIA</journal-title></journal-title-group><issn pub-type="ppub">1067-5027</issn><issn pub-type="epub">1527-974X</issn><publisher><publisher-name>BMJ Group</publisher-name><publisher-loc>BMA House, Tavistock Square, London, WC1H 9JR</publisher-loc></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">22494789</article-id><article-id pub-id-type="pmc">3392849</article-id><article-id pub-id-type="publisher-id">amiajnl-2011-000631</article-id><article-id pub-id-type="doi">10.1136/amiajnl-2011-000631</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research and Applications</subject></subj-group><series-title>FOCUS on clinical research informatics</series-title></article-categories><title-group><article-title>Using ontology-based annotation to profile disease research</article-title></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><name><surname>Liu</surname><given-names>Yi</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Coulet</surname><given-names>Adrien</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="aff" rid="aff2">2</xref></contrib><contrib contrib-type="author"><name><surname>LePendu</surname><given-names>Paea</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib><contrib contrib-type="author"><name><surname>Shah</surname><given-names>Nigam H</given-names></name><xref ref-type="aff" rid="aff1">1</xref></contrib></contrib-group><aff id="aff1"><label>1</label>Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, California, USA</aff><aff id="aff2"><label>2</label>LORIA–INRIA Nancy, Vandoeuvre-lès-Nancy Cedex, France</aff><author-notes><corresp><bold>Correspondence to</bold> Yi Liu, Stanford University, Stanford Center for Biomedical Informatics, 1265 Welch Road, Medical School Office Building, Room X215 Mail Code 5479, Stanford, CA 94305, USA; <email>liuyipei@stanford.edu</email></corresp></author-notes><pub-date pub-type="epub"><day>11</day><month>4</month><year>2012</year></pub-date><pub-date pub-type="ppub"><month>6</month><year>2012</year></pub-date><pub-date pub-type="pmc-release"><day>1</day><month>6</month><year>2013</year></pub-date><pmc-comment> PMC Release delay is 12 months and 0 days and was based on the
<volume>19</volume><issue>e1</issue><fpage>e177</fpage><lpage>e186</lpage><history><date date-type="received"><day>10</day><month>10</month><year>2011</year></date><date date-type="accepted"><day>8</day><month>3</month><year>2012</year></date></history><permissions><copyright-statement>© 2012, Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.</copyright-statement><copyright-year>2012</copyright-year></permissions><self-uri xlink:title="pdf" xlink:type="simple" xlink:href="amiajnl-2011-000631.pdf"></self-uri><abstract><sec><title>Background</title><p>Profiling the allocation and trend of research activity is of interest to funding agencies, administrators, and researchers. However, the lack of a common classification system hinders the comprehensive and systematic profiling of research activities. This study introduces ontology-based annotation as a method to overcome this difficulty. Analyzing over a decade of funding data and publication data, the trends of disease research are profiled across topics, across institutions, and over time.</p></sec><sec><title>Results</title><p>This study introduces and explores the notions of research sponsorship and allocation and shows that leaders of research activity can be identified within specific disease areas of interest, such as those with high mortality or high sponsorship. The funding profiles of disease topics readily cluster themselves in agreement with the ontology hierarchy and closely mirror the funding agency priorities. Finally, four temporal trends are identified among research topics.</p></sec><sec><title>Conclusions</title><p>This work utilizes disease ontology (DO)-based annotation to profile effectively the landscape of biomedical research activity. By using DO in this manner a use-case driven mechanism is also proposed to evaluate the utility of classification hierarchies.</p></sec></abstract><kwd-group><kwd>Applications that link biomedical knowledge from diverse primary sources (includes automated indexing)</kwd><kwd>bioinformatics</kwd><kwd>collaborative technologies</kwd><kwd>informatics</kwd><kwd>knowledge discovery</kwd><kwd>knowledge representations</kwd><kwd>machine learning</kwd><kwd>medical records</kwd><kwd>methods for integration of information from disparate sources</kwd><kwd>natural language processing</kwd><kwd>ontologies</kwd><kwd>ontology</kwd><kwd>pharmacogenomics</kwd><kwd>statistical analysis of large datasets</kwd><kwd>text mining</kwd></kwd-group></article-meta></front></pmc><affiliations><list><country><li>France</li><li>États-Unis</li></country><region><li>Californie</li></region></list><tree><country name="États-Unis"><region name="Californie"><name sortKey="Liu, Yi" sort="Liu, Yi" uniqKey="Liu Y" first="Yi" last="Liu">Yi Liu</name></region><name sortKey="Coulet, Adrien" sort="Coulet, Adrien" uniqKey="Coulet A" first="Adrien" last="Coulet">Adrien Coulet</name><name sortKey="Lependu, Paea" sort="Lependu, Paea" uniqKey="Lependu P" first="Paea" last="Lependu">Paea Lependu</name><name sortKey="Shah, Nigam H" sort="Shah, Nigam H" uniqKey="Shah N" first="Nigam H" last="Shah">Nigam H. Shah</name></country><country name="France"><noRegion><name sortKey="Coulet, Adrien" sort="Coulet, Adrien" uniqKey="Coulet A" first="Adrien" last="Coulet">Adrien Coulet</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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