1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Discovery Seminar 025087/UU – Spring 2008 Translational Pharmacogenomics: Discovering New Genetic Methods to Link Diagnosis and Drug Treatment Ontology: Developing a Systematic Approach to Translational Pharmacogenomic Research Data Collection April 16, 2008 Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences Ontology Research Group University at Buffalo, NY, USA

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U What does the term ‘ontology’ mean ?

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Google  ‘define: ontology’: the study of the broadest range of categories of existence, which also asks questions about the existence of particular kinds of objects; an explicit representation of the meaning of terms in a vocabulary, and their relationships; a common vocabulary for describing the concepts that exist in an area of knowledge and the relationships that exist between them; specification of a conceptualisation of a knowledge domain; a structured information model of a domain capable of supporting reasoning by human users and software agents; a data model that represents a set of concepts within a domain and the relationships between those concepts; …

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U One term, many definitions This raises some questions: –Is it possible for a term to have so many meanings? –Can the authors of these definitions all be right at the same time? –Is it possible for something to which one of these definitions applies to be such that also one or more of the other definitions apply ?

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Is it possible for a term to have so many meanings? Merriam-Webster on ‘bank’ Entry term 27 occurrence types 3 different word types –Noun –Verb –Part of compound term http://www.merriam-webster.com/dictionary }

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Is it possible for a term to have so many meanings? Merriam-Webster on ‘bank’ http://www.merriam-webster.com/dictionary

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Is it possible for a term to have so many meanings? Merriam-Webster on ‘bank’ http://www.merriam-webster.com/dictionary

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Is it possible for a term to have so many meanings? Clearly: yes ! This phenomenon is called: Homonymy

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Term  Meaning Term-1 Meaning-3 Meaning-4 Meaning-1 Meaning-5 Meaning-6 Meaning-7 Meaning-2 Term-2 Term-3 This is called:synonymy

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Homonymous use of the term ‘ontology’ the study of the broadest range of categories of existence, which also asks questions about the existence of particular kinds of objects; an explicit representation of the meaning of terms in a vocabulary, and their relationships; a common vocabulary for describing the concepts that exist in an area of knowledge and the relationships that exist between them; specification of a conceptualisation of a knowledge domain; a structured information model of a domain capable of supporting reasoning by human users and software agents; a data model that represents a set of concepts within a domain and the relationships between those concepts; …

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Can the authors of these definitions all be right at the same time? Yes, if we are dealing with a case of homonymy.

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Is it possible for something to which one of these definitions applies to be such that also one or more of the other definitions apply ? study representation vocabulary specification information model data model ‘that’ thing is an is a ?

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Is it possible for something to which one of these definitions applies to be such that also one or more of the other definitions apply ? Not for all ! Only for some

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Homonymous use of the term ‘ontology’: at least one clear cut distinction the study of the broadest range of categories of existence, which also asks questions about the existence of particular kinds of objects; an explicit representation of the meaning of terms in a vocabulary, and their relationships; a common vocabulary for describing the concepts that exist in an area of knowledge and the relationships that exist between them; specification of a conceptualisation of a knowledge domain; a structured information model of a domain capable of supporting reasoning by human users and software agents; a data model that represents a set of concepts within a domain and the relationships between those concepts; …

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Ontology’ as the study of what exists Key questions: –What exists ? –How do things that exist relate to each other ? Some hypotheses: –An external reality, time, space –Ideas, concepts –Particulars, universals, objects, processes –God Ontologists from distinct ‘schools’ differ in opinion about the existence of some of the above: –Realism, nominalism, conceptualism, monism, …

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U An ontology as a representation Terms  WordNet, MedDRA Concepts  the majority of ‘ontologies’ But … overwhelming lack of clarity about what ‘concepts’ are: meaning shared in common by synonymous terms ? idea shared in common in the minds of those who use these terms ? unit of knowledge describing meanings ? feature or property shared in common by entities in the world ? Universals  Realism-based ontology Key question: of what ?

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology & Translational Pharmacogenomics

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Pharmacogenomics the branch of pharmacology whichpharmacology –deals with the influence of genetic variation on drug response in patientsgenetic –by correlating gene expression or single-nucleotide polymorphisms with a drug's efficacy or toxicity.gene expressionsingle-nucleotide polymorphismsefficacytoxicity By doing so, pharmacogenomics aims to develop rational means to optimise drug therapy, with respect to the patients' genotype, to ensure maximum efficacy with minimal adverse effects.genotypeadverse effects

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Translational research is the movement of discoveries in basic research (the Bench) to application at the clinical level (the Bedside). transforms scientific discoveries arising from laboratory, clinical, or population studies into clinical applications to reduce disease incidence, morbidity, and mortality.

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Key challenge: understanding how disorders at molecular level lead to disorders at mesoscopic level

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U How to get there ? Current mainstream thinking data information knowledge wisdom Reality What is there on the side of the patient

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example of data generation at the bench Amit Sheth. Semantic Web Technology in Support of Bioinformatics for Glycan Expression. W3C workshop on Semantic Web for Life Sciences, October 28, 2004, Cambridge MA

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Semantic annotation of Scientific Data Amit Sheth. Semantic Web Technology in Support of Bioinformatics for Glycan Expression. W3C workshop on Semantic Web for Life Sciences, October 28, 2004, Cambridge MA

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontologies for annotating data Snomed-CT view on Serum hepatitis

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Zoom on Hepatitis B with hepatitis D superinfection Relationships to other concepts: – Causative agent Hepatitis D virus (organism) – Finding site Liver structure (body structure) – Causative agent Hepatitis B virus (organism) – Associated morphology Inflammation (morphologic abnormality) Information about this concept: –PREFERRED_TERM Hepatitis B with hepatitis D superinfection –TERM Hepatitis B with delta agent coinfection –TERM Hepatitis B with delta agent superinfection –TERM Hepatitis B with hepatitis D superinfection –TERM Hepatitis D infection –TERM Viral hepatitis B with delta agent superinfection –TERM Viral hepatitis B with hepatitis D superinfection Comments ?

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U SNOMED-CT generated taxonomy (partial) General finding of abdomen (finding) Abdominal organ finding (finding) Liver finding (finding) Disorder of liver (disorder) Inflammatory disease of liver (disorder) Viral hepatitis (disorder) Type B viral hepatitis (disorder) Hepatitis B with hepatitis D superinfection (disorder) Disorder of abdomen (disorder) Infectious disease of abdomen (disorder) Viscus structure finding (finding) Is a Comments ?

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Problem with mainstream thinking: data information knowledge wisdom Questions not often enough asked: What part of our data corresponds with something out there in reality ? What part of reality is not captured by our data, but should because it is relevant ? Reality What is there on the side of the patient

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The solution: the RIGHT sort of ontology Realism-based ontology

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Realist ontology: assumes three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; 3.To make these representations publicly accessible in some enduring fashion, they create representational artifacts that are fixed in some medium. Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality exists before any observation R

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality exists before any observation Humans had a brain well before they knew they had one. Trees were green before humans started to use the word “green”. R And also most structures in reality are there in advance.

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The cognitive agent acknowledges the existence of some Portion of Reality (POR) R B

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U R B Some portions of reality escape his attention.

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; 3.To make these representations publicly accessible in some enduring fashion, they create representational artifacts that are fixed in some medium. Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U R He represents only what he considers relevant Both RU 1 B1 and RU 1 O1 are representational units referring to #1; RU 1 O1 is NOT a representation of RU 1 B1 ; RU 1 O1 is created through concretization of RU 1 B1 in some medium. O B #1 RU 1 B1 RU 1 O1

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A realism-based ontology is a representation of some pre-existing domain of reality which –(1) reflects the properties of the objects within its domain in such a way that there obtains a systematic correlation between reality and the representation itself, –(2) is intelligible to a domain expert –(3) is formalized in a way that allows it to support automatic information processing

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Our foundations: Basic Formal Ontology An ontology which is –Realist: –Fallibilist: –Perspectivalist: –Adequatist: There is only one reality and its constituents exist independently of our (linguistic, conceptual, theoretical, cultural) representations thereof, theories and classifications can be subject to revision, there exists a plurality of alternative, equally legitimate perspectives on that one reality these alternative views are not reducible to any single basic view.

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Basic Formal Ontology The world consists of –entities that are Either particulars or universals; Either occurrents or continuants; Either dependent or independent; and, –relationships between these entities of the form e.g. is-instance-of, e.g. is-member-of e.g. isa (is-subtype-of) Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Accept that everything may change: 1.changes in the underlying reality: Particulars and universals come and go 2.changes in our (scientific) understanding: The planet Vulcan does not exist 3.reassessments of what is considered to be relevant for inclusion (notion of purpose). 4.encoding mistakes introduced during data entry or ontology development.

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Example: continuants preserve identity while changing caterpillarbutterfly animal t human being living creature me child Instance-of in 1960 adult me Instance-of since 1980

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Are we done ? Is an accurate coding system, classification system, terminology, ontology, …, a necessary and sufficient condition for obtaining “better” information ? Necessary: yes ! Sufficient: no !

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract Three references of hypertension for the same patient denote three times the same disease. If two different fracture codes are used in relation to observations made on the same day for the same patient, they might refer to the same fracture The same type of location code used in relation to three different events might or might not refer to the same location. If the same fracture code is used for the same patient on different dates, then these codes might or might not refer to the same fracture. The same fracture code used in relation to two different patients can not refer to the same fracure. If two different tumor codes are used in relation to observations made on different dates for the same patient, they may still refer to the same tumor. Using codes does not prevent ambiguities as to what is described: how many disorders are listed?

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Consequences Very difficult to: –Count the number of (numerically) different diseases Bad statistics on incidence, prevalence,... Bad basis for health cost containment –Relate (numerically the same or different) causal factors to disorders: –Dangerous public places (specific work floors, swimming pools), –dogs with rabies, –HIV contaminated blood from donors, –food from unhygienic source,... Hampers prevention –...

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking: Being clear what the data are about !

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Purpose: –explicit reference to the concrete individual entities relevant to the accurate description of each patient’s condition, therapies, outcomes,... Now! That should clear up a few things around here ! Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking: Numbers instead of words! Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78. Method: –Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity 78

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘John Doe’s ‘John Smith’s liver tumor was treated with RPCI’s irradiation device’ ‘John Doe’s liver tumor was treated with RPCI’s irradiation device’ The principle of Referent Tracking #1 #3 #2 #4 #5 #6 treating person liver tumor clinic device instance-of at t 1 #10 #30 #20 #40 #5 #6 inst-of at t 2 inst-of at t 2 inst-of at t 2 inst-of at t 2 inst-of at t 2

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U EHR – Ontology “collaboration”

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Advantage: better reality representation 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract IUI-001 IUI-003 IUI-004 IUI-005 IUI-007 IUI-002 IUI-012 IUI-006 7 distinct disorders

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Did you pay attention ? A final test In John Smith’s EHR: –At t 1 : “male”at t 2 : “female” What are the possibilities ? Change in reality: transgender surgery change in legal self-identification Change in understanding: it was female from the very beginning but interpreted wrongly Correction of data entry mistake: it was understood as male, but wrongly transcribed

53. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusion (1) Building high quality ontologies is hard. Not everybody has the right skills –Experts in driving cars are not necessarily experts in car mechanics (and the other way round). Ontologies should represent the state of the art in a domain, i.e. the science. –Science is not a matter of consensus or democracy Natural language relates more to how humans talk about reality or perceive it, than to how reality is structured. No high quality ontology without the involvement of ontologists.

54. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusion (2) Realist ontology is a powerful QA tool for building high quality ontologies AND high quality databases; Referent tracking, based on realist ontology, is a means to remove the ambiguity in data that cannot be solved by realist ontology alone; –It is a form of “adult” annotation Application of RT requires a globally accessible repository –Adds another level to interoperability. The use of “meaningless” IUIs allows very strict safety and security measures to be implemented.

55. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Goal: new form of Evidence Based Medicine Now: –Decisions based on (motivated/justified by) the outcomes of (reproducable) results of well-designed studies Guidelines and protocols –Evidence is hard to get, takes time to accumulate. Future: –Each discovered fact or expressed belief should instantly become available as contributing to ‘evidence’, wherever its description is generated. –Data ‘eternally’ reusable independent of the purpose for which they have been generated.