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The Problem of Reusability of Biomedical Data OBO Foundry & HL7 RIM

The Problem of Reusability of Biomedical Data OBO Foundry & HL7 RIM. Barry Smith. DCRI Project Goal. to facilitate research in CV and TB by increasing the re-usability of data collected in the healthcare setting. Knowledge Environments for Biomedical Research (KEBR).

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The Problem of Reusability of Biomedical Data OBO Foundry & HL7 RIM

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  1. The Problem of Reusability of Biomedical DataOBO Foundry & HL7 RIM Barry Smith

  2. DCRI Project Goal • to facilitate research in CV and TB by increasing the re-usability of data collected in the healthcare setting. http://ontology.buffalo.edu/smith

  3. Knowledge Environments for Biomedical Research (KEBR) • NIH Conference, December 11-12, 2006 • Knowledge environments must be characterized by: • sustainability • adaptability • interoperability • evolvability http://ontology.buffalo.edu/smith

  4. sustainability • biologists have huge amounts of data, which they need to manage and make accessible • have worked out a sustainable way of achieving this result http://ontology.buffalo.edu/smith

  5. adaptability • best achieved through modularity, • each portion of the knowledge environment controlled by appropriate domain experts http://ontology.buffalo.edu/smith

  6. interoperability • the modules should use a common (simple) logic and a common, thoroughly-tested (simple) ontology • - unification with a light touch http://ontology.buffalo.edu/smith

  7. evolvability • = change in light of scientific advance •  knowledge environments must be tied to biological and clinical research • must be able to evolve incrementally • must • must ensure backwards compatibility with legacy annotations http://ontology.buffalo.edu/smith

  8. how do we make different sorts of data combinablein ways useful to the human beings who carry out research? http://ontology.buffalo.edu/smith

  9. how was this problem solved in the years before computers? • how did clinical researchers from different disciplines communicate? • how did they learn to communicate? http://ontology.buffalo.edu/smith

  10. Ontology-based methodology • for clinical and translational research http://ontology.buffalo.edu/smith

  11. through the basic biomedical sciencesanatomy, physiology, biochemistry, histology, ... http://ontology.buffalo.edu/smith

  12. what is “metadata” ?? • data models (HL7 RIM, BRIDG, ...  UML) • vs. • ontologies • How should we represent data? • vs. • How should we represent reality? http://ontology.buffalo.edu/smith

  13. http://ontology.buffalo.edu/smith

  14. what cellular component? what molecular function? what biological process? http://ontology.buffalo.edu/smith

  15. Gene Ontology http://ontology.buffalo.edu/smith

  16. checking the ontology: everything can be traced back to instances in reality • serotonin is_a biogenic amine • every instance of serotonin is an instance of biogenic amine http://ontology.buffalo.edu/smith

  17. Heparin therapy is_a written or spoken designation of a concept • mouse is_a common name for the species mus musculus • virus is_a environment ontology • unclassified Lulworthiales is_a environmental samples http://ontology.buffalo.edu/smith

  18. Logical power of the ontology Example: Ontologies facilitate grouping of annotations brain 20 hindbrain15 rhombomere 10 Query brain without ontology 20 Query brain with ontology 45 http://ontology.buffalo.edu/smith

  19. Biorepository Ontology • Chemical Entities of Biological Interest (ChEBI) • Clinical Investigation Ontology (CIO) • Common Anatomy Reference Ontology (CARO) • Disease Ontology (DO) • Foundational Model of Anatomy (FMA) • Cell Ontology (CL) • Gene Ontology (GO) • Mosquito Anatomy Ontology (MAO) • Ontology for Biomedical Investigations (OBI) • Phenotypic Quality Ontology (PaTO) • Plant Ontology (PO) • Protein Ontology (PRO) • Relation Ontology (RO) • RNA Ontology (RnaO) • Sequence Ontology (SO) • Xenopus Anatomy Ontology (XAO) • Zebrafish Anatomical Ontology (ZAO) http://ontology.buffalo.edu/smith

  20. Chemical Entities of Biological Interest (ChEBI) • Clinical Investigation Ontology (CIO) • Common Anatomy Reference Ontology (CARO) • Disease Ontology (DO) • Foundational Model of Anatomy (FMA) • Cell Ontology (CL) • Gene Ontology (GO) • Mosquito Anatomy Ontology (MAO) • Ontology for Biomedical Investigations (OBI) • Phenotypic Quality Ontology (PaTO) • Plant Ontology (PO) • Protein Ontology (PRO) • Relation Ontology (RO) • RNA Ontology (RnaO) • Sequence Ontology (SO) • Xenopus Anatomy Ontology (XAO) • Zebrafish Anatomical Ontology (ZAO) http://ontology.buffalo.edu/smith

  21. Biorepository Ontology • Chemical Entities of Biological Interest (ChEBI) • Clinical Investigation Ontology (CIO) • Common Anatomy Reference Ontology (CARO) • Disease Ontology (DO)  interoperation with SNOMED CT • Foundational Model of Anatomy (FMA) • Cell Ontology (CL) • Gene Ontology (GO) • Mosquito Anatomy Ontology (MAO) • Ontology for Biomedical Investigations (OBI) • Phenotypic Quality Ontology (PaTO)  signs and symptoms • Plant Ontology (PO) • Protein Ontology (PRO) • Relation Ontology (RO) • RNA Ontology (RnaO) • Sequence Ontology (SO) • Xenopus Anatomy Ontology (XAO) • Zebrafish Anatomical Ontology (ZAO) http://ontology.buffalo.edu/smith

  22. Building out from the original GO http://ontology.buffalo.edu/smith

  23. BFO Top-Level Ontology Continuant Occurrent (always dependent on one or more independent continuants) Independent Continuant Dependent Continuant http://ontology.buffalo.edu/smith

  24. = A representation of top-level types Continuant Occurrent biological process biological process Independent Continuant Dependent Continuant cell component molecular function http://ontology.buffalo.edu/smith

  25. Top-Level Ontology Continuant Occurrent Independent Continuant Dependent Continuant Quality Function instances (in space and time) http://ontology.buffalo.edu/smith

  26. BFO as organising structure http://ontology.buffalo.edu/smith

  27. http://obofoundry.org • clinical medicine rooted in the basic biological sciences via high-quality controlled vocabularies http://ontology.buffalo.edu/smith

  28. next step: create a repertoire of disease ontologiesbuilt out of OBO Foundry elements http://ontology.buffalo.edu/smith

  29. Ontology for Acute Respiratory Distress Syndrome http://ontology.buffalo.edu/smith

  30. what data do we have? what data do the others have? what data do we not have? Draft Ontology for Multiple Sclerosis http://ontology.buffalo.edu/smith

  31. HL7 http://hl7-watch.blogspot.com/ http://ontology.buffalo.edu/smith

  32. Schadow • The RIM ‘defines the grammar of a language for information in healthcare’. • ‘All data is in a form in which Entities (people, places, things: NOUNS) are related in Roles (RELATORS) to other Entities, and through their participations (PREPOSITIONS) interact in Acts (VERBS).’ http://ontology.buffalo.edu/smith

  33. Problems of scope • Act = intentional action • No processes (verb items) outside Act • How can the RIM deal with disease processes, drug interactions, traffic accidents, adverse events? http://ontology.buffalo.edu/smith

  34. Problems of scope • Entity = persons, places, organizations, material • No things (noun items) outside Entity • How can the RIM deal with wounds, fractures,? • How can the RIM deal with diseases? http://ontology.buffalo.edu/smith

  35. Mayo on ‘Act’ as “intentional action” • Is a snake bite or bee sting an intentional action? • Is a knife stabbing an intentional action? • Is a car accident an intentional action? • When a child swallows the contents of a bottle of poison is that an intentional action? http://informatics.mayo.edu/wiki/index.php/Intentionality_of_Act_and_the_Future_ of_Observations http://ontology.buffalo.edu/smith

  36. Diseases in the RIM • ... are not Acts • ... are not Entities • ... are not Roles, Participations, Role-Links ... • So what are they? http://ontology.buffalo.edu/smith

  37. Correct Answer • Diseases fall outside the scope of the RIM. The RIM is concerned to standardize the way in which data is represented in messages, etc. It is not concerned to standardize the way in which diseases, alleles, drug interactions, etc., are represented. http://ontology.buffalo.edu/smith

  38. The RIM’s answer • Diseases are Acts of Observation • A case of pneumonia is an Act of Observation of a case of pneumonia • A diagnosis is an Act of Observation of an Act of Observation http://ontology.buffalo.edu/smith

  39. HL7’s Clinical Genomics Standard Specifications • an individual allele as an Act of Observation • a phenotype is an Act of Observation of an Act of Observation http://ontology.buffalo.edu/smith

  40. What should be done? • Create a clinical ontology which allows adequate treatment of all the types of entity relevant to information exchange in biomedicine, including: • non-intentional processes, diseases, infections, biomolecules, etc. http://ontology.buffalo.edu/smith

  41. BFO top-Level Ontology Continuant Occurrent Independent Continuant Dependent Continuant Act Physical Event ... ... Quality Function http://ontology.buffalo.edu/smith

  42. RIM Ontology Continuant Occurrent Entity (Intentional) Act Bio- molecule Disease Drug interaction ... Person Physical Thing Organ- ization http://ontology.buffalo.edu/smith

  43. BFO normalized RIM Continuant Occurrent Independent Continuant Dependent Continuant Act Physical Event Everything made of molecules Condition Request Observation Drug interaction Temperature Disease http://ontology.buffalo.edu/smith

  44. What is new Continuant Occurrent Independent Continuant Dependent Continuant Act Physical Event Everything made of molecules Condition Request Observation Drug interaction Temperature Disease http://ontology.buffalo.edu/smith

  45. Coherent interoperation with ChEBI, PATO, SNOMED, MedDRA, etc. Continuant Occurrent Independent Continuant Dependent Continuant Act Physical Event Everything made of molecules Condition Request Observation Drug interaction Temperature Disease http://ontology.buffalo.edu/smith

  46. ? ? ? ? ? ? http://ontology.buffalo.edu/smith

  47. what data do we have? what data do the others have? what data do we not have? Draft Ontology for Multiple Sclerosis http://ontology.buffalo.edu/smith

  48. Methodology of cross-products • compound terms and definitions should be built out of constituent terms drawn from ontologies. E.g. • PaTO increased concentration’ • FMA ‘blood’ • CheBI term ‘glucose’ •  blood glucose phenotypes. • Foundry provides rigor for post-coordination • Contributions to solving the silo problem http://ontology.buffalo.edu/smith

  49. Open questions • relations to • generating forms • controlled vocabulary for clinical care • common data elements • clinical trials • treatments • signs and symptoms • (clinical and pre-clinical manifestations) http://ontology.buffalo.edu/smith

  50. Open questions • role of • stakeholders • professional society support • champions who will test • role of rare disease researcher communities • mandates http://ontology.buffalo.edu/smith

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