Amia 2013: How can bio-ontologies support clinical and translational science?

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Amia 2013: How can bio-ontologies support clinical and translational science?

  1. 1. Leveraging ontologies for research reproducibility, resource sharing, and researcher networking Carlo Torniai and Melissa Haendel Ontology Development Group Oregon Health & Science University Oregon Health & Science3/19/2013 1 University
  2. 2. Topics  Research reproducibility  Ontology driven application for research resource identification and sharing  Team science  Shared and computable expertise in support of research profiling and building translational teams Oregon Health & Science3/19/2013 2 University
  3. 3. Supporting the translational lifecycle Bench experiments Research Clinical Trials resources Shared Knowledge Clinical Publish Research papers Registries and Databases Oregon Health & Science3/19/2013 3 University
  4. 4. eagle-i: inventories “invisible” resources Ontology-system for collecting and querying research resources eagle-i.net net w o r k Oregon Health & Science3/19/2013 4 University
  5. 5. eagle-i: ontology driven application Oregon Health & Science3/19/2013 5 University
  6. 6. eagle-i: ontology driven application Oregon Health & Science3/19/2013 6 University
  7. 7. Leveraging ontologies for resource representation  Enables classification and unique reference of resources in the literature and in clinical protocols  Enables linkage with other standard vocabularies and ontologies (MeSH, Gene Ontology, ICD)  Facilitates semantic connections between resources, people, and clinical research  Standard representation of research resources enables inference of expertise Oregon Health & Science3/19/2013 7 University
  8. 8. What is expertise? Oregon Health & Science3/19/2013 8 University
  9. 9. Leveraging expertise  Innovation happens between publications  Team science has a higher impact  Clinical expertise isn’t well represented by publications or grants We need a system that can connect basic and clinical researchers Oregon Health & Science3/19/2013 9 University
  10. 10. CTSAConnect: using ontologies to connect clinical and basic researchersGoals: – Identify potential collaborators, relevant resources, and expertise across disciplines – Assemble translational teams of scientists to address specific research questionsApproach: Create a semantic system to enable: – Broad and computable representation of translational expertise – publication of expertise as Linked Data (LD) for use in other applications Oregon Health & Science3/19/2013 10 University
  11. 11. CTSAConnect Semantic People VIVO VIVO Coordination Clinical eagle-i eagle-i activities Resources eagle-i is an ontology-driven application . . . for collecting and searching research resources. VIVO is an ontology-driven application . . . for collecting and displaying information about people. Both publish Linked Data. Neither addresses clinical expertise. CTSAconnect will produce a single Integrated Semantic Framework, a modular collection of ontologies — that also includes clinical expertise Oregon Health & Science3/19/2013 11 University
  12. 12. Ontologies refactoring ShareCenter VIVO Discussions, requests, eagle-i Person profiling share documents Research resources ISF Organizations Clinical ReagentsContact Services Events Credentials Affiliations Expertise Organisms Oregon Health & Science 3/19/2013 12 University
  13. 13. Clinical Expertise Generation Step 1 Step 2 Step 3 Step 4 Aggregate Compute Map Data to Publish Linked Clinical Data Expertise Ontologies Data Unique 3 PatientProvider ID ICD Code Value Code Count Count Code Label Unilateral or unspecified femoral hernia with 1234567 552.00 1 1 obstruction (ICD9CM 552.00) Bilateral femoral hernia without mention of 1234567 553.02 8 6 obstruction or gangrene (ICD9CM 553.02) Regional enteritis of large 1234567 555.1 4 1 intestine (ICD9CM 555.1) Corrected transposition of 1234568 745.12 10 5 great vessels (ICD9CM 745.12) 1 2 4 Come to my talk tomorrow at 11.30 Electronic Health Record Data Mining
  14. 14. Putting it all together No “biological” relationships between Stanley and Kelsey Oregon Health & Science3/19/2013 14 University
  15. 15. Our dream scenarioResearchers are connected based on relationships between resources, publications,projects, pathways, phenotypes, etc. Oregon Health & Science 3/19/2013 15 University
  16. 16. Monarch Initiative www.monarchininitiative.org Come see our poster this Afternoon. N. 51 Enabling phenotype-based knowledge discovery tools Oregon Health & Science3/19/2013 16 University
  17. 17. Translational cross-institutional search Type I diabetes mellitus reference Pathway FAS diabetes Autoimmune B6-H2g7 Lymphoproliferative Syndrome Mus musculus Evaluation of Dermal Myelinated Fibers K. Hattori in Patients with Diabetic Polyneuropathy P. Kurre CTSA1 A. PeltierFigure form Keggwww.genome.jp/kegg-bin/show_pathway?map04940 Oregon Health & Science 3/19/2013 17 University
  18. 18. Resources eagle-i Support : NCRR / NCATS through http://eagle-i.net Booz-Allen Hamilton #U24 RR 029825 CTSAconnect Support : NCATS through Booz Allen Hamilton http://ctsaconnect.org CTSA 10-001: 100928SB23 Monarch Initiative Support : NIH Office of the Director 1R24OD011883-01 http://monarchinitiative.org Oregon Health & Science3/19/2013 18 University

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