Transcript
Page 1: Richard H. Scheuermann U.T. Southwestern Medical Center

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

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Outline

• Motivation - CTSA• Ontologies and OBO Foundry• Ontology for Biomedical Investigations (OBI)• Ontology for Clinical Investigations (OCI)

– Approach – Current status– Future direction

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Clinical and Translational Science Award (CTSA)

Implementing biomedical discoveries Implementing biomedical discoveries made in the last 10 years demands an made in the last 10 years demands an evolution of clinical science.evolution of clinical science.

New prevention strategies and treatments New prevention strategies and treatments must be developed, tested, and brought must be developed, tested, and brought into medical practice more rapidly.into medical practice more rapidly.

CTSA awards will lower barriers between CTSA awards will lower barriers between disciplines, and encourage creative, disciplines, and encourage creative, innovative approaches to solve complex innovative approaches to solve complex medical problems.medical problems.

These clinical and translational science These clinical and translational science awards will catalyze change -- breaking awards will catalyze change -- breaking silos, breaking barriers, and breaking silos, breaking barriers, and breaking conventions.conventions.

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Trial Design

Advanced Degree-Granting

Programs

Participant& CommunityInvolvement

RegulatorySupport

Biostatistics

ClinicalResources

BiomedicalInformatics

ClinicalResearch

Ethics

CTSACTSAHOMEHOME

NIH & other government

agencies

Healthcare organizations

IndustryIndustry

Each academic health center will create a home for clinical and translational science

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Building a National CTSA Consortium

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• 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

Clinical Research Information System - utCRIS

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High Level Design Vision

External Collaborators

External BioinformaticsData Sources - Entrez Gene - Uniprot - dbSNP - GEO/Array

Data Mining

Reporting

XML FeedsWeb Forms

HL7 IE

ETL Virtual Web Community

UTSW Researchers

Clinical Data

Reference Data

Experiment Data

Proposal Development & Tracking

Trial Recruiting

Protocol Management

Clinical Trials Management

CRF Development

Clinical Research

Data WarehouseutCR-DW

CTMSData

SecurityBiostatistics

Security

Security

Security

Patient Registries

Tissue Data Banks

Private Clinical Data

PACS

Other Clinical Data

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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)

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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

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Need for standard representations

• Minimum information sets• Standard vocabularies/ontologies• Standard data models

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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)

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• 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

Ontology Goals

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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

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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

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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

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The OBO Foundry

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RELATION TO TIME

GRANULARITY

CONTINUANT OCCURRENT

INDEPENDENT DEPENDENT

ORGAN ANDORGANISM

Organism(NCBI

Taxonomy / placeholder

)

Anatomical Entity(FMA, CARO)

OrganFunction

(placeholder) Phenotypic

Quality(PaTO)

Biological Process

(GO)CELL AND CELLULAR

COMPONENT

Cell(CL)

Cellular Compone

nt(FMA, GO)

Cellular Function

(GO)

MOLECULEMolecule

(ChEBI, SO,RnaO, PrO)

Molecular Function(GO)

Molecular Process

(GO)

Initial OBO Foundry Ontologiesbuilding out from the original GO

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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)

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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)

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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

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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 Ontology

Multiple Sclerosis Ontology

Mammalian Adult Neurogenesis Ontology

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Ontology for BioMedical InvestigationsOBI

(previously FuGO)

Name of the presenter here

On behalf of the

OBI Coordination Committee

Name of the meeting here

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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


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