cabig: the cancer biomedical informatics grid ken buetow ncicb/nci/nih/dhhs

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caBIG: the cancer Biomedical Informatics Grid Ken Buetow NCICB/NCI/NIH/DHHS

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caBIG: the cancer

Biomedical Informatics Grid

Ken BuetowNCICB/NCI/NIH/DHHS

NCI biomedical informatics

Goal: A virtual web of interconnected data, individuals, and organizations redefines how research is conducted, care is provided, and patients/participants interact with the biomedical research enterprise

•Trials•Animal Models

states

context•pathways•ontologies

agents•therapeutics•probes

components•genes•genotypes•gene expression•proteins•protein expression

etiology,treatment,prevention

MolecularPathology

ClinicalTrials

caCORE

accessportals

participatinggroup nodes

CancerGenomicsMouse

Models

building common architecture, common tools, and common standards

Interoperability

SemanticSemanticinteroperabilityinteroperability

SyntacticSyntacticinteroperabilityinteroperability

Courtesy: Charlie Mead

in·ter·op·er·a·bil·i·ty- ability of a system...to use the parts or equipment of another systemSource: Merriam-Webster web site

interoperability- ability of two or more systems or components to exchange information and to use the information that has been exchanged.Source: IEEE Standard Computer Dictionary: A Compilation of IEEE Standard Computer

Glossaries, IEEE, 1990]

Enterprise Vocabulary

NCI Meta-Thesaurus (Cross-map standard vocabularies/ontologies, e.g. SNOMED, MEDRA, ICD)- Semantic integration, inter-vocabulary

mapping- UMLS Metathesaurus extended with

cancer-oriented vocabularies• 800,000 Concepts, 2,000,000

terms and phrases• Mappings among over 50

vocabularies

NCI Thesaurus- Description logic-based- 18,000 “Concepts”

• Concept is the semantic unit• One or more terms describe a

Concept – synonymy• Semantic relationships between

Concepts

biomedical objects

common data elements

controlled vocabulary

Common Data Elements

Structured data reporting elements

Precisely defining the questions and answers- What question are you

asking, exactly?- What are the possible

answers, and what do they mean?

biomedical objects

common data elements

controlled vocabulary

Biomedical Information Objects

Data service infrastructure developed using OMG’s Model Driven Architecture approach

Object models expressed in UML represent actual biomedical research entities such as genes, sequences, chromosomes, sequences, cellular pathways, ontologies, clinical protocols, etc.

The object models form the basis for uniform APIs (Java, SOAP, HTTP-XML, Perl) that provide an abstraction layer and interfaces for developers to access information without worrying about the back-end data stores

biomedical objects

common data elements

controlled vocabulary

Standards supporting infrastructure

Enterprise Vocabulary Services (EVS)- Browsers- APIs

cancer Bioinformatics Infrastructure Objects (caBIO)- Applications- APIs

cancer Data Standards Repository (caDSR)- CDEs- Case Report Forms- Object models- ISO 11179 model

Data AccessObjects

Object Managers

DomainObjects

RMI

Web Server

TomcatServlets

JSPsSOAP XML

XSL/XSLT

HTML (Browsers)

SOAP Clients

Java Applications

DataObjectPresentationClient

Integrating Architecture

HTML/XML Clients

Meta-Data

PERLClients

Semantic Integration: Modeling Time

Class

Attributes

EVS Concept for Attribute ‘agentName’

EVS Concept for Class ‘Agent’

EVS Concept for Attribute ‘id’

.

.

.etc.

EVS Conceptfor instance objects

Object

Mapping to EVS Concepts Done at Modeling Time

Semantic Integration:Metadata Registration Time

UML model, including EVS Concept mappings

ISO11179 mapping

caDSR loading

Curation: Data standards registration

for instance data

Semantic Integration: Runtime

Java Applications

Data AccessObjects(OJB)

Object Managers

Web Server

TomcatServlets

( XMLXSL/XSLT )

JSPs

SOAP

HTML/XML Clients

(Browsers)

SOAP Clients

DataObjectPresentationClient

Perl Clients

Domain Objects[Gene, Disease,

Concept,DataElement]

RMI

ResearchDBs

Research

DBs

caGRID caCORE architecture extension

caBIO server

caBIO client

OGSA-DAI + Globus

Globus

OGSA-DAIcaGRID extension

(metadata)

caGRID extension (caBIO adapter)

caGRID extension(query)

Client

Grid

Data Source

caGRID extension (Concept Discovery)

caGRID extension (Federated Query)

caGRID Extension (Integration of Discovery and Query Services)

NCICB applications:• clincial trials support - C3DS

• molecular pathology - caArray• cancer images - caImage • pre-clinical models - caModelsDb• laboratory support - caLIMS

Standards-based Data System for the conduct of clinical trials:

• C3D (Cancer Central Clinical Database)– WWW-based eCRF-based primary data capture by protocol

• C3PR (Cancer Central Clinical Participant Registry)– WWW-based Central registration of participants across

protocols • C3PA (Cancer Central Clinical Protocol Administration)

– Scientific management system for clinical protocols • C3TR (Cancer Central Clinical Tissue Repository)

– Tissue repository • C3DW (Cancer Central Clinical Data Warehouse)

– De-identified patient information accessed via caBIO

Image Portal• The NCICB has

developed an image portal to allow researchers to search for mouse and human images and annotations– Human and

mouse images and annotations were provided by the MMHCC

Pathway Database • Enhance value of imperfect, but

available, pathway knowledge• Make biological assumptions

explicit• Combine sources of data (e.g.

KEGG, BioCarta, ...)• Merge data from separate

pathways• Build a causal framework to

support (future) quantitative simulation/analysis

Cancer Biomedical Informatics Grid (caBIG)

Common, widely distributed infrastructure permits cancer research community to focus on innovation

Shared vocabulary, data elements, data models facilitate information exchange

Collection of interoperable applications developed to common standard

Raw published cancer research data is available for mining and integration

caBIG will facilitate sharing of infrastructure, applications, and data

caBIG action plan Establish pilot network of Cancer Centers

- Groups agreeing to caBIG principles- Mixture of capabilities- Mixture of contributions

Expanding collection of participants Establish consortium development process

- Collecting and sharing expertise- Identifying and prioritizing community

needs- Expanding development efforts

Moving at the speed of the internet…

Three Domain Workspaces and two Cross Cutting Workspaces have been launched during the Pilot

phase

DOMAIN WORKSPACE 3Tissue Banks & Pathology ToolsDOMAIN WORKSPACE 3Tissue Banks & Pathology Tools

provides for the integration, development, and implementation of tissue and pathology tools.

DOMAIN WORKSPACE 2Integrative Cancer ResearchDOMAIN WORKSPACE 2Integrative Cancer Research

provides tools and systems to enable integration and sharing of information.

DOMAIN WORKSPACE 1Clinical Trial Management SystemsDOMAIN WORKSPACE 1Clinical Trial Management Systems

addresses the need for consistent, open and comprehensive tools for clinical trials management.

CROSS CUTTING WORKSPACE 2Architecture

CROSS CUTTING WORKSPACE 2Architecture

developing architectural standards and architecture necessary for other workspaces.

CROSS CUTTING WORKSPACE 1Vocabularies & Common

Data Elements

CROSS CUTTING WORKSPACE 1Vocabularies & Common

Data Elements

responsible for evaluating, developing, and integrating systems for vocabulary and ontology content, standards, and software systems for content delivery

Key deliverables of caBIG pilot

Componentized, standards-based Clinical Trials Management System- e-IND filing/regulatory reporting with FDA- Electronic management of trials- Integration of diverse trials

Tissue Management System- Systematic description and characterization of tissue

resources- Ability to link tissue resources to clinical and molecular

correlative descriptions “Plug and Play” analytic tool set

- microarray- proteomics- pathways- data analysis and statistical methods- gene annotation

Diverse library of raw, structured data

Cancer Molecular Analysis Project (CMAP)

- a prototypic biomedical data integration effort

biomedical objects

common data elements

controlled vocabulary

Profiles, Targets, Agents, Clinical Trials

CGAPNCBIUCSC

(via DAS)

BioCarta

KEGGGeneOntologies

CTEP clinical trials

CGAP gene expression

NCI drug screening

NCI drug screening

caBIG community contributions

Infrastructure- Ontologies- Databases

Applications- Clinical trials

support- Analytic tools- Data mining

Data- Trials- Experimental

outcomes•Genomic•Microarray•Proteomic

acknowledgements

NCICB- Peter Covitz- Sue Dubman- Mary Jo Deering- Leslie Derr- Carl Schaefer- Christos Andonyadis- Mervi Heiskanen - Denise Hise- Kotien Wu- Fei Xu- Frank Hartel

LPG/CCR- Michael Edmundson- Bob Clifford- Cu Nguyen

http://ncicb.nci.nih.gov

http://cmap.nci.nih.gov

http://caBIG.nci.nih.gov