Richard H. Scheuermann U.T. Southwestern Medical Center

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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. Outline. Motivation - CTSA Ontologies and OBO Foundry - PowerPoint PPT Presentation


  • Ontology for Clinical Investigations (OCI): Representation of clinical research data in the framework of a formal biomedical investigation ontology

    Richard H. ScheuermannU.T. Southwestern Medical Center

  • OutlineMotivation - CTSAOntologies and OBO FoundryOntology for Biomedical Investigations (OBI)Ontology for Clinical Investigations (OCI)Approach Current statusFuture direction

  • 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 lowerbarriers 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.

  • Each academic health center will create a home for clinical and translational scienceTrial DesignAdvanced Degree-GrantingProgramsParticipant& CommunityInvolvement

    RegulatorySupportBiostatisticsClinicalResourcesBiomedicalInformaticsClinicalResearchEthicsCTSAHOMENIH & other government agenciesHealthcare organizationsIndustry

  • Building a National CTSA Consortium

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

  • High Level Design VisionExternal CollaboratorsExternal BioinformaticsData Sources - Entrez Gene - Uniprot - dbSNP - GEO/ArrayData MiningReportingXML FeedsWeb Forms

    HL7 IEETLVirtual Web CommunityUTSW ResearchersClinical DataReference DataExperiment DataProposal Development & TrackingTrial Recruiting Protocol ManagementClinical Trials ManagementCRF DevelopmentClinical Research Data WarehouseutCR-DWCTMSDataSecurityBiostatisticsSecuritySecuritySecurity

  • RequirementsAccurate Representationtherapeutic drug as a design variable vs. medical historyDNA as a therapeutic agent vs. analysis specimenInteroperabilityunambiguous data exchange between research siteseffective data exchange between software applicationsCustomizationsupport of study-specific detailsDynamicsRole changes throughout and between studiesInferenceSemantic queries (e.g. patients with autoimmune disease)Meta-analysisStudies with common features (e.g. all studies where flu vaccine was evaluated as a conditional variable)

  • ConstraintsEssential 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 ITSupport wide variety of different clinical and translational study types - reduce complexity by modeling commonalitiesSupport needs of multiple stakeholders - different uses of same dataStandards should be easy to implement and useStandards need to be easily and logically extensibleSupport clinical research data use cases

  • Need for standard representationsMinimum information setsStandard vocabularies/ontologiesStandard data models

  • Definition of OntologyPhilosophicalThe 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 scientistsA 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 vocabularyA 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)

  • 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 definedOntology Goals

  • Problems with existing ontologiesOverlapping domainsDevelopment within a vacuumInteroperability ontologies should be able to work together and be used by other ontologiesCurrent ontologies do not deal well with time and spaceLack of well-defined relationshipsLack of widespread use and acceptanceBuilt based on varying principles

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

  • The OBO Foundrya 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*

  • The OBO Foundry

  • *Initial OBO Foundry Ontologiesbuilding out from the original GO


    GRANULARITYCONTINUANTOCCURRENTINDEPENDENTDEPENDENTORGAN ANDORGANISMOrganism(NCBITaxonomy / placeholder)Anatomical Entity(FMA, CARO)OrganFunction(placeholder)Phenotypic Quality (PaTO)Biological Process(GO)CELL AND CELLULAR COMPONENTCell(CL)Cellular Component(FMA, GO)Cellular Function(GO)MOLECULEMolecule(ChEBI, SO,RnaO, PrO)Molecular Function(GO)Molecular Process(GO)

  • *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)

  • *Ontologies being built to satisfy Foundry principles ab initioCommon 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)

  • *Foundry ontologies all work in the same waywe have datawe need to make this data available for semantic search and algorithmic processingwe create a consensus-based ontology for annotating the dataand ensure that it can interoperate with Foundry ontologies for neighboring domains

  • OBO Foundry provides a suite of basic science Reference Ontologiesdesigned to serve as modules for re-use in Application Ontologies such as:Infectious Disease OntologyImmunology OntologyMultiple Sclerosis OntologyMammalian Adult Neurogenesis Ontology


  • Ontology for BioMedical InvestigationsOBI

    (previously FuGO)Name of the presenter here

    On behalf of the OBI Coordination Committee Name of the meeting here

  • 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

    The first 12 CTSAs were awarded in 2006 to 12 academic health centers located throughout the nation. When fully implemented in 2012, the initiative is expected to provide a total of about $500 million annually to 60 academic health centers. An additional 52 academic health centers received planning grants, in 2006, to help them prepare applications to join the consortium. Enabling infrastructureOne group may not describe the same domain in the same way. GO part_of can mean 2 different things. Add more to this slideStrict regimentation*


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