1. a School of Information Science, Federal University of Minas Gerais, Brazil b Medical University of Graz, Austria, c University Medical Center Freiburg, Germany d European Bioinformatics Institute, Hinxton, UK; e University of Geneva, Switzerland f Helsinki University, Finland, g Uniquer, Lausanne, Switzerland Requirements for Semantic Biobanks André Q ANDRADE a,b,, Markus KREUZTHALER b, Janna HASTINGS d,e, Maria KRESTYANINOVA f,g, Stefan SCHULZ b,c

2. Semantic interoperability: systems exchange exchange data + meaning Formal Ontologies provide unambiguous descriptions of what is universally true for all objects of a certain type Increasing number of biomedical vocabularies are ontology based (OBO Foundry, SNOMED CT…) Blood, tissue sampling for research Samples from several biobanks needed for retrieving data for a specific research question Comprehensive annotations with lab data and clinical data Biobanks Semantic Model of MeaningData

3. (Generalized) Biomedical Retrieval Scenario Retrieval: –Distribution of heterogeneous resources of interest –Most retrieval scenarios recall-oriented Resources used by multiple researchers over the world for multiple purposes Effective retrieval depends on querying resource metadata –Provenance information –Content-based semantic annotations (structured vocabulary) –Access regulations Does this sound familiar?

4. Analogy

5. Global bibliographic database Resources: publications from different publishers Annotations: –Bibliographic data –Abstract –Semantic representation (MeSH) on paper content Local access conditions to the full resource apply

6. Analogy Global bibliographic database Resources: publications from different publishers Annotations: –Bibliographic data –Abstract –Semantic representation (MeSH) of paper content Local access conditions to the full resource apply Biobank “Broker” Global biobank sample database Resources: biological specimens (blood, tissue,…) Annotations: –Sample information (staining etc…) –Semantic representation of both lab and selected patient related information (Information models / ontologies) Local access conditions to the full resource apply

7. Sample related information: – Type of sample – Preparation of sample – Time – Storage information – Physical location – Associated information, lab data, genotype,… Donor related information: – Demographic data – Phenotype data – Time indexed clinical data (EHR extracts) Increment of relevant donor related information after samples are taken Data resources for biobanking 1960 1970 1980 1990 2000 2010

8. Centralized broker for biobanking information + Biobank * + EHR + Biobank * + EHR + Biobank * + EHR + Biobank * + EHR

9. Centralized broker for biobanking information + Biobank * + EHR + Biobank * + EHR + Biobank * + EHR + Biobank * + EHR

10. Centralized broker for biobanking information + Biobank * + EHR + Biobank * + EHR + Biobank * + EHR + Biobank * + EHR

11. Language for semantic annotations of biobank data Formal ontologies –Precise, logical descriptions of annotations and queries –High expressiveness through compositionality –OWL-DL: Semantic Web Standard for description logics: allows to formulate axioms of what is universally true of all instances of a kind Specific components –Ground axioms provided by an upper level ontology (BioTop) –Set of disjoint upper level categories and relations, together with related constraints –Ontological description of domain: SNOMED CT, OBO Foundry…

12. BioTop categories and example axiom

13. Description logics representation and retrieval retrieves “retrieve all gastric mucosa samples from before 2003 of patients who had cancer of stomach after 2008” Representation language: OWL DL Editor: Protégé 4.2. Reasoner: HermiT

14. Requirements Formal representations –Ontological representation of information models and terminologies –Ontological representation of data about specimens –Joint, universally used clinical terminology –Expressive and stable upper level ontologies (+ ontological relations) Scope and granularity of EHR extract of interest for biobank related queries Specification of structure and function of central repository Steps for information translation from legacy systems –Mappings –Interfaces –Update policies

15. Challenges Prototypical status of DL reasoners and editor Performance problems with expressive ontologies Modularization of large clinical terminologies in response to data and query under scrutiny Organization of –Central repository –Local mappings / translations Logistics (samples) Privacy and IP issues Business model

16. Thanks CAPES (Brazil) – Programa de Doutorado no País com Estágio no Exterior FP7 – NoE SemanticHealthNet Andrade et al.: Requirements for Semantic Biobanks