Richard H. Scheuermann U.T. Southwestern Medical Center

1. Ontology for Clinical Investigations (OCI): Representation of clinical research data in the framework of a formal biomedical investigation ontology Richard H. Scheuermann U.T. Southwestern Medical Center

2. Outline • Motivation - CTSA • Ontologies and OBO Foundry • Ontology for Biomedical Investigations (OBI) • Ontology for Clinical Investigations (OCI) • Approach • Current status • Future direction

3. Clinical and Translational Science Award (CTSA) • Implementing biomedical discoveries made in the last 10 years demands an evolution of clinical science. • New prevention strategies and treatments must be developed, tested, and brought into medical practice more rapidly. • CTSA awards will lower barriers between disciplines, and encourage creative, innovative approaches to solve complex medical problems. • These clinical and translational science awards will catalyze change -- breaking silos, breaking barriers, and breaking conventions.

4. Each academic health center will create a home for clinical and translational science NIH & other government agencies Clinical Research Ethics Trial Design Advanced Degree-Granting Programs Biomedical Informatics CTSA HOME Industry Clinical Resources Participant & Community Involvement Biostatistics Regulatory Support Healthcare organizations

5. Building a National CTSA Consortium

6. Clinical Research Information System - utCRIS • Data management - to develop a comprehensive controlled information system infrastructure to capture and manage clinical and translational research data • Data integration - to integrate clinical and translational research data with data and knowledge from external public database resources • Data analysis - to support clinical and translational research data analysis by providing state-of-the-art software analytical tools • Support - to provide training and support for CRIS use

7. Private Clinical Data Tissue Data Banks Patient Registries PACS Other Clinical Data Proposal Development & Tracking High Level Design Vision Protocol Management UTSW Researchers CRF Development Trial Recruiting Security Clinical Trials Management Security CTMS Data Clinical Research Data Warehouse utCR-DW Security Reporting Virtual Web Community Clinical Data Data Mining ETL Biostatistics HL7 IE Security XML Feeds Web Forms Experiment Data External Bioinformatics Data Sources - Entrez Gene - Uniprot - dbSNP - GEO/Array External Collaborators Reference Data

8. Requirements • Accurate Representation • therapeutic drug as a design variable vs. medical history • DNA as a therapeutic agent vs. analysis specimen • Interoperability • unambiguous data exchange between research sites • effective data exchange between software applications • Customization • support of study-specific details • Dynamics • Role changes throughout and between studies • Inference • Semantic queries (e.g. patients with autoimmune disease) • Meta-analysis • Studies with common features (e.g. all studies where flu vaccine was evaluated as a conditional variable)

9. Constraints • Essential to build upon and extend, or map to, existing and emerging data standards (e.g. HL7, CDISC, ICD, UMLS, Epoch, RCT Schema, NCI Thesaurus, SNOMED-CT, etc.) • Recognize the difference between Health IT and Research IT • Support wide variety of different clinical and translational study types - reduce complexity by modeling commonalities • Support needs of multiple stakeholders - different uses of same data • Standards should be easy to implement and use • Standards need to be easily and logically extensible • Support clinical research data use cases

10. Need for standard representations • Minimum information sets • Standard vocabularies/ontologies • Standard data models

11. Definition of “Ontology” • Philosophical • “The study of that which exists” (ISMB 2005) • “The science of what is: of the kinds and structures of the objects, and their properties and relations in every area of reality” (ISMB 2005) • Information/computer scientists • “A shared, common, backbone taxonomy of relevant entities, and the relationships between them, within an application domain” (ISMB 2005) • “A computable representation of biological reality” (ISMB 2005) • “A structured vocabulary” • “A formal way of representing knowledge in which concepts are described both by their meaning and their relationship to each other” (Bard 2004) • “A data model that represents a domain and is used to reason about the objects in that domain and the relations between them” (Wikipedia)

12. Ontology Goals • Provide clear thinking about how to structure information • Support data integration, modeling, query processing, user interface development, data exchange/export • To enforce data correctness • To be able to map to database management systems • To enables a computer to reason over the data • To provide the capability to infer relationships that have not been explicitly defined

13. Problems with existing ontologies • Overlapping domains • Development within a vacuum • Interoperability – ontologies should be able to work together and be used by other ontologies • Current ontologies do not deal well with time and space • Lack of well-defined relationships • Lack of widespread use and acceptance • Built based on varying principles

14. Defining ontology principles: The OBO Foundry - 2006 The OBO foundry is a set of interoperable ontologies that adhere to a growing set of principles set forth for best practices in ontology development

15. The OBO Foundry a voluntary initiative of developers of consensus biomedical ontologies designed to be interoperable, logically coherent, biologically accurate and subject to update in light of scientific advance 15

16. The OBO Foundry

17. Initial OBO Foundry Ontologies building out from the original GO 17

18. Mature OBO Foundry ontologies (now undergoing reform) Cell Ontology (CL) Chemical Entities of Biological Interest (ChEBI) Foundational Model of Anatomy (FMA) Gene Ontology (GO) Phenotypic Quality Ontology (PaTO) Relation Ontology (RO) Sequence Ontology (SO) 18

19. Ontologies being built to satisfy Foundry principles ab initio Common Anatomy Reference Ontology (CARO) Environment Ontology (EnvO / GEO) Ontology for Biomedical Investigations (OBI) Ontology for Clinical Investigations (OCI, part of OBI) Protein Ontology (PRO) RNA Ontology (RnaO) 19

20. Foundry ontologies all work in the same way we have data we need to make this data available for semantic search and algorithmic processing we create a consensus-based ontology for annotating the data and ensure that it can interoperate with Foundry ontologies for neighboring domains 20

21. OBO Foundry provides a suite of basic science Reference Ontologies designed to serve as modules for re-use in Application Ontologies such as: Infectious Disease Ontology Immunology Ontology Multiple Sclerosis Ontology Mammalian Adult Neurogenesis Ontology 21

22. Ontology for BioMedical InvestigationsOBI(previously FuGO) Name of the presenter here On behalf of the OBI Coordination Committee Name of the meeting here

23. OBI - Overview • International collaboration (since 2006) • Communities developing ontologies/terminologies • Unambiguous description of how the investigation was performed • Consistent annotation, powerful queries and data integration • Describe the laboratory workflow • Set of universal terms - Investigation (organization, intent, design etc) • Material (biological and chemical, manipulation and transformation) - Protocols and instrumentations • Data generated and types of analysis performed on it • Set of biological and technological domain-specific terms - To meet the annotation requirements of any given community • Part of the Open Biomedical Ontology (OBO) Foundry • Orthogonality and x-referencing with existing bio-ontologies • 'Interoperable by construction'with those under the Foundry - Including Unit, Quality (PATO), Environment and Chemical (ChEBI) ontologies