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Mapping to Ontologies

Mapping to Ontologies. Nigam Shah nigam@stanford.edu. NCBO: Key activities. We create and maintain a library of biomedical ontologies. We build tools and Web services to enable the use of ontologies and their derivatives.

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Mapping to Ontologies

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  1. Mapping to Ontologies Nigam Shah nigam@stanford.edu

  2. NCBO: Key activities • We create and maintain a library of biomedical ontologies. • We buildtools and Web services to enable the use of ontologies and their derivatives. • We collaborate with scientific communities that develop and use ontologies.

  3. www.bioontology.org

  4. Total Monthly Visits to BioPortal

  5. Download • Traverse • Search • Comment Ontology Services Views • Create • Download • Upload Mapping Services http://rest.bioontology.org • Tree-view • Auto-complete • Graph-view Widgets Annotation Term recognition Fetch “data” annotated with a given term Data Access http://bioportal.bioontology.org

  6. Mappings R Root Term-1 Term-2 t1 t2 Term-2 t1 Term-3 Term-4 t3 Term-5 t5 t4 t5 t7 Term-5 t6 Ontology B Ontology A Upload or Download mapping subsets

  7. Annotation as a Web service Process textual metadata to automatically tag text with as many ontology terms as possible.

  8. Multiple ways to access Code Excel Elsevier UIMA platform Specific UI 98 million calls, ~900 GB of data Annotator service

  9. Annotation Analytics - I Analysis of semantically tagged data

  10. Mining Annotations of Grants, Publications • Publications from Medline • Only “Journal articles” Grants from 1972 to 2007 30 funding agencies

  11. BioPortal + Protégé are tools for collaborative, shared development of such hierarchies (ontologies).

  12. Degree of Sponsorship

  13. Allocation of Funding

  14. Who funds what

  15. Credits Mark Musen, PI • The team @ www.bioontology.org/project-team NIH Roadmap grant U54 HG004028

  16. Annotation Analytics - II Analysis of semantically tagged data

  17. Generation of tagged data Text clinical note BioPortal – knowledge graph Creating clean lexicons Term – 1 : : : Term – n Frequency Diseases Annotation Workflow Term recognition tool NCBO Annotator NegEx Patterns Procedures Syntactic types Drugs Terms Recognized NegExRules – Negation detection Further Analysis Negation detection Cohort of Interest Terms form a temporal series of tags 

  18. Adverse drug events ROR of 2.058, CI of [1.804, 2.349] PRR of 1.828, CI of [1.645, 2.032] The uncorrected X2 statistic has p-value < 10-7. ROR=1.524, CI=[0.872, 2.666] PRR=1.508, CI=[0.8768, 2.594] X2 p-value=0.06816.

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