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caBIG: the cancer Biomedical Informatics Grid

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Title: caBIG: the cancer Biomedical Informatics Grid


1
caBIG the cancer Biomedical Informatics Grid
  • Ken Buetow
  • NCICB/NCI/NIH/DHHS

2
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

3
etiology,treatment,prevention
4
building common architecture, common tools, and
common standards
accessportals
participatinggroup nodes
ClinicalTrials
MolecularPathology
caCORE
CancerGenomics
MouseModels
5
Interoperability
Courtesy Charlie Mead
  • interoperability
  • 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

Semanticinteroperability
Syntacticinteroperability
6
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
7
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
8
Biomedical Information Objects
  • Data service infrastructure developed using OMGs
    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
9
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

10
Integrating Architecture
Data
Object
Presentation
Client
Domain Objects
HTML (Browsers)
Web Server
Tomcat Servlets JSPs SOAP XML XSL/XSLT
HTML/XML Clients
RMI
Object Managers
SOAP Clients
Meta-Data
Data Access Objects
PERLClients
Java Applications
11
Semantic Integration Modeling Time
Class
Attributes
Mapping to EVS Concepts Done at Modeling Time
12
Semantic IntegrationMetadata Registration Time
ISO11179 mapping
caDSR loading
UML model, including EVS Concept mappings
Curation Data standards registration for
instance data
13
Semantic Integration Runtime
Presentation
Client
Data
Object
HTML/XML Clients (Browsers)
Web Server
Domain Objects Gene, Disease, Concept, DataElemen
t
Research DBs
Tomcat Servlets ( XML XSL/XSLT ) JSPs SOAP
SOAP Clients
Research DBs
RMI
Object Managers
Perl Clients
Data Access Objects (OJB)
Java Applications
14
caGRID caCORE architecture extension
caGRID Extension (Integration of Discovery and
Query Services)
OGSA-DAI Globus
caGRID extension (Concept Discovery)
caGRID extension (Federated Query)
Client
OGSA-DAI
caGRID extension (metadata)
caGRID extension (query)
Grid
Globus
caGRID extension (caBIO adapter)
caBIO client
Data Source
caBIO server
15
NCICB applications
  • clincial trials support - C3DS
  • molecular pathology - caArray
  • cancer images - caImage
  • pre-clinical models - caModelsDb
  • laboratory support - caLIMS

16
  • 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

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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

20
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

21
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

22
caBIG will facilitate sharing of infrastructure,
applications, and data
23
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

24
Three Domain Workspaces and two Cross Cutting
Workspaces have been launched during the Pilot
phase
DOMAIN WORKSPACE 1 Clinical Trial Management
Systems
addresses the need for consistent, open and
comprehensive tools for clinical trials
management.
DOMAIN WORKSPACE 2 Integrative Cancer Research
provides tools and systems to enable integration
and sharing of information.
DOMAIN WORKSPACE 3 Tissue Banks Pathology Tools
provides for the integration, development, and
implementation of tissue and pathology tools.
CROSS CUTTING WORKSPACE 1 Vocabularies Common
Data Elements
responsible for evaluating, developing, and
integrating systems for vocabulary and ontology
content, standards, and software systems for
content delivery
CROSS CUTTING WORKSPACE 2 Architecture
developing architectural standards and
architecture necessary for other workspaces.
25
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

26
Cancer Molecular Analysis Project (CMAP)- a
prototypic biomedical data integration effort
Profiles, Targets, Agents, Clinical Trials
NCBI
CGAP
CTEP clinical trials
UCSC (via DAS)
NCI drug screening
CGAP gene expression
KEGG
GeneOntologies
BioCarta
NCI drug screening
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caBIG community contributions
  • Infrastructure
  • Ontologies
  • Databases
  • Applications
  • Clinical trials support
  • Analytic tools
  • Data mining
  • Data
  • Trials
  • Experimental outcomes
  • Genomic
  • Microarray
  • Proteomic

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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 h
ttp//caBIG.nci.nih.gov
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