How Bio Ontologies Enable Open Science


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How Bio Ontologies Enable Open Science

  1. 1. How Bio-Ontologies enable Open Science Nigam Shah [email_address]
  2. 2. Ontologies By Pedro Beltrão
  3. 3. Key Points <ul><li>Open science requires structured content. </li></ul><ul><li>Structured content acquisition runs into a curation bottleneck. </li></ul><ul><ul><li>And “controlled manual curation” will not scale </li></ul></ul><ul><li>For “open science” to really take off: </li></ul><ul><ul><li>collaborative curation platforms are going to be necessary and, </li></ul></ul><ul><ul><li>(semi-)automation of curation is going to be necessary. </li></ul></ul><ul><li>Researchers need to exactly identify what is being mentioned/discussed. </li></ul><ul><li>NCBO provides services that support these needs </li></ul>
  4. 4. <ul><li>Currently, the main use of ontologies is for making sense of high throughput data. </li></ul>There are other uses of course, see Biomedical Ontologies: A functional perspective, Rubin et al, Briefings in Bioinformatics, Dec 2007, Vol 9:1 75-90
  5. 5. How does ontology help? <ul><li>An ontology provides a organizing framework for creating “abstractions” of the high throughput data </li></ul><ul><li>The simplest ontologies (i.e. terminologies, controlled vocabularies) provide the most bang-for-the-buck </li></ul><ul><ul><li>Gene Ontology (GO) is the prime example </li></ul></ul><ul><li>More structured ontologies – such as those that represent pathways and more higher order biological concepts – still have to demonstrate real utility. </li></ul>
  6. 6. Ontologies and content acquisition <ul><li>First start naming ‘things’ </li></ul><ul><li>Then name ‘relationships’ </li></ul><ul><li>Then comes the ‘logic of combining simple relationships’ </li></ul><ul><li>… realization that all this “structure” is hard to create manually and manual curation will not scale … lots of dead projects. </li></ul><ul><ul><li>Leads to new found love for text-mining! </li></ul></ul>
  7. 7. Emerging trends in content acquisition <ul><li>Increased Structure (in curation and annotations) </li></ul><ul><li>Collaborative curation platforms </li></ul><ul><ul><li>Knewco </li></ul></ul><ul><ul><li>SWAN </li></ul></ul><ul><ul><li>CBioC </li></ul></ul><ul><ul><li>… </li></ul></ul><ul><li>Integration of Text-mining in curation </li></ul><ul><ul><li>Finding entities </li></ul></ul><ul><ul><ul><li>BioLit by Phil Bourne’s group </li></ul></ul></ul><ul><ul><li>Finding relations … facts. </li></ul></ul><ul><ul><ul><li>Larry Hunter’s group </li></ul></ul></ul><ul><ul><ul><li>Biolink papers </li></ul></ul></ul><ul><ul><ul><li>EBI-MED </li></ul></ul></ul>
  8. 8. Increasing Structure <ul><li>Until now the predominant use of ontologies is as a vocabulary to describe data … minimal structure in the descriptions. </li></ul><ul><li>Precise capture of biomedical knowledge in structured form is now considered essential </li></ul><ul><ul><li>Hits the manual curation bottleneck. </li></ul></ul><ul><ul><li>WA Baumgartner Jr. et al, Manual curation is not sufficient for annotation of genomic databases. Bioinformatics 2007 23(13):i41-i48. Presented at ISMB 2007 </li></ul></ul>
  9. 9. Knewco: Concept Web and Wikiprofesional
  10. 10. The SWAN discourse ontology Ciccarese P, Wu E, Clark T (2007) 'An Overview of the SWAN 1.0 Ontology of Scientific Discourse‘ at the 16th International World Wide Web Conference Banff, Canada. May 8-12, 2007.
  11. 11. Collaborative KB curation: SWAN Knowledge Workbench Copyright 2007 Alzheimer Research Forum and Massachusetts General Hospital
  12. 12. Copyright 2007 Alzheimer Research Forum and Massachusetts General Hospital
  13. 13. Copyright 2007 Alzheimer Research Forum and Massachusetts General Hospital
  14. 14. The SWAN Team and papers <ul><li>Harvard/MGH : Paolo Ciccarese, Marco Ocana, Tim Clark </li></ul><ul><li>Alzforum : Elizabeth Wu, Gwen Wong, June Kinoshita </li></ul>Copyright 2007 Alzheimer Research Forum and Massachusetts General Hospital [1] Gao Y, Kinoshita J, Wu E, Miller E, Lee R, Seaborne A, Cayzer S, Clark T (2006) ‘ SWAN: A Distributed Knowledge Infrastructure for Alzheimer Disease Research’. Journal of Web Semantics 4(3).   [2] Ciccarese P, Wu E, Clark T (2007) 'An Overview of the SWAN 1.0 Ontology of Scientific Discourse'. 16th International World Wide Web Conference (WWW2007). Banff, Canada. May 8-12, 2007. [3] Clark T and Kinoshita J (2007) 'Alzforum and SWAN: The Present and Future of Scientific Web Communities'. Briefings in Bioinformatics 8(3). [4] Ciccarese, P, Wu E, Kinoshita J, Wong G, Ocana M, Ruttenberg A and Clark T (submitted for publication 9/4/2007) 'The SWAN Ontology of Scientific Discourse'. Photo not available
  15. 15. Integration of Text-mining + Curation <ul><li>Text mining works better if it uses appropriate ontologies. </li></ul><ul><li>“ Model” mismatch b/w needs of text mining and needs of KB builders. </li></ul><ul><li>Text mining might work much better if: </li></ul><ul><ul><li>It works in a loop with a curator </li></ul></ul><ul><ul><li>It leverages the wisdom of the masses </li></ul></ul>
  16. 16. Integration of Text-mining + Curation
  17. 19. Quick recap <ul><li>Use of ontologies in collaborative curation and content acquistion is not wide-spread; possibly because of: </li></ul><ul><ul><li>Lack of a one stop shop for bio-ontologies </li></ul></ul><ul><ul><li>Lack of tools to use ontologies for annotation </li></ul></ul><ul><ul><ul><li>Manual  will not scale </li></ul></ul></ul><ul><ul><ul><li>Automatic  can it be ‘good enough’? </li></ul></ul></ul><ul><ul><li>Lack of a sustainable mechanism to create ontology based annotations </li></ul></ul>
  18. 20. NCBO’s efforts <ul><li>The key ingredients needed for collaborative curation platforms to succeed: </li></ul><ul><ul><li>Proper use of bioontologies (just enough ontology!) </li></ul></ul><ul><ul><li>Appropriate use of Natural Language Processing in the curation workflow. </li></ul></ul><ul><li>NCBO has created web-services that allow use of ontologies in collaborative platforms </li></ul>
  19. 21. NCBO ontology services Base URL: Documentation: Description REST URL List all ontologies ./ontologies Find a specific ontology ./ontologies/{ontology version id} Download ontology file ./ontologies/download/{ontology version id} Get versions of an ontology ./ontologies/version/{ontology id} Get concept ./concepts/{ontology version id}/{concept id} Search for concepts ./search/concepts/{query}?ontologies={ids} Get latest version of an ontology ./virtual/{ontology_id} Get concept for latest ontology version ./virtual/{ontology id}/{concept id} List all ontology categories ./categories
  20. 22. NCBO annotation services <ul><li>Open Biomedical Annotator (OBA) web service </li></ul><ul><ul><li>To automatically process textual metadata to recognize relevant ontology concepts and return the terms as annotations </li></ul></ul><ul><li>Open Biomedical Resource (OBR) index </li></ul><ul><ul><li>To index the contents of a few biomedical resources with the biomedical concepts to which they relate … and allow programmatic access to the indexed data. </li></ul></ul><ul><li>URL: </li></ul>
  21. 23. ANNOTATOR SERVICE <ul><li>Using Ontologies to Annotate Your Data </li></ul>
  22. 24. Annotator: The Basic Idea <ul><li>Process textual metadata to automatically tag text with as many ontology terms as possible. </li></ul>
  23. 25. Annotator: Usage <ul><li>Give your text as input </li></ul><ul><li>Select your parameters (ontologies to use, semantic type to filter, semantic expansion…) </li></ul><ul><li>Get your results… in text, tab-delimited, XML, or OWL </li></ul><ul><li>Paper in AMIA STB 09 </li></ul>
  24. 26. DATA SERVICE <ul><li>Using Ontologies to Access and Analyze Public Data </li></ul>
  25. 27. Open Biomedical Resources index <ul><li>The index can be used for: </li></ul><ul><ul><li>Search (next few slides) </li></ul></ul><ul><ul><li>Data mining (Paper in AMIA STB 08 on mining relationships b/w drugs, diseases and genes from Medline) </li></ul></ul>
  26. 28. Example
  27. 32. NCBO services Ontology services (OBS) UMLS services BioPortal services Data service (OBR) Annotation service (OBA) Users UCSF Laboratree CollabRx PharmGKB, JAX HGMD Users BioPortal UI PDB/PLoS I2B2 NextBio IO informatics Users “ Resources” tab Knewco IO informatics
  28. 33. Uses of NCBO services <ul><li>For programmatic access to latest versions of ontologies </li></ul><ul><li>For concept recognition from text </li></ul><ul><ul><li>For annotation </li></ul></ul><ul><ul><li>For accelerating curation </li></ul></ul><ul><li>For data aggregation and summarization </li></ul>
  29. 34. BioLit web resource: automated recognition of ontology terms and database IDs after publication
  30. 35. Automated recognition of ontology terms and database IDs before publication with manual curation by author Word 2007 add-in
  31. 36. End
  32. 37. Annotation: UCSF <ul><li>The task is to decide which trial is relevant for a particular patient. </li></ul><ul><ul><li>Use the annotator service to map concepts in eligibility rules to UMLS CUIs </li></ul></ul><ul><ul><li>Use the annotations from the OBR index to create tag clouds in CTExplorer. </li></ul></ul>
  33. 38. Annotation: Laboratree
  34. 39. Annotation: CollabRx caTissue/TIES Specimen Banking Specimen management is based on ontologies developed by NCI Ontology-based integration to create a virtual specimen bank
  35. 40. Curation: JAX, UCHSC, PDB/PLoS <ul><li>JAX – Use concepts recognized in the abstracts of publications to triage papers for curation. </li></ul><ul><li>UCSHC – Wrap our annotator as a UIMA component and compare performance on full text </li></ul><ul><li>PDB/PLoS – BioLit and Word-plugin </li></ul>
  36. 41. Ontology Access: I2B2 <ul><li>Needs a “source” for ontologies in their ontology cell </li></ul><ul><li>Using our services, we export BioPortal Ontologies to the I2B2 format. </li></ul>
  37. 42. Ontology Access: IO-informatics
  38. 43. Ontology Access: NextBio <ul><li>“ Our collaboration with NCBO on adopting public biomedical ontologies throughout NextBio enabled us to create a platform dealing with heterogeneous biological data. These ontology-based search capabilities have resulted in a rapid adoption of NextBio by over 100,000 researchers around the world since our public debut in May of 2008”. </li></ul>
  39. 44. Data Summarization: PharmGKB 1. CYP2C9 , 2. VKORC1 , 3. CYP2A6 , etc. 1. Hemorrhage , 2. Venous Thrombosis , etc. 1. warfarin , 2. coumarin , 3. phenoprocoumon , etc. 34 scored annotations: 5 scored annotations: 20 scored annotations:
  40. 45. Data Summarization: Knewco
  41. 46. Data Summarization: HGMD <ul><li>Use the disease hierarchy from SNOMED-CT to compute “enrichment” of mutation types in particular types of diseases </li></ul><ul><li>… playing the GO-based microarray analysis game for disease mutations </li></ul>