Transcript caBIG: the cancer Biomedical Informatics Grid Ken Buetow NCICB/NCI/NIH/DHHS NCI biomedical informatics  Goal: A virtual web of interconnected data, individuals, and organizations redefines how research is conducted,

caBIG: the cancer
Biomedical
Informatics Grid
Ken Buetow
NCICB/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
context
•pathways
•ontologies
components
•genes
•genotypes
•gene
expression
•proteins
•protein
expression
etiology,
treatment,
prevention
states
•Trials
•Animal Models
agents
•therapeutics
•probes
building common architecture, common tools, and common standards
access
portals
Clinical
Trials
participating
group nodes
Molecular
Pathology
caCORE
Mouse
Models
Cancer
Genomics
Interoperability
Courtesy: Charlie Mead
 in·ter·op·er·a·bil·i·ty
- ability of a system...to use the parts or
equipment of another system
Source: 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]
Syntactic
interoperability
Semantic
interoperability
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 backend 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
Integrating Architecture
Client
HTML
(Browsers)
HTML/XML
Clients
SOAP
Clients
PERL
Clients
Java
Applications
Object
Presentation
Domain
Objects
Web Server
Tomcat
Servlets
JSPs
SOAP
XML
XSL/XSLT
Data
RM
I
Object
Managers
Data
Access
Objects
Meta-Data
Semantic Integration: Modeling Time
Class
EVS Concept for Class ‘Agent’
EVS Concept for Attribute ‘id’
EVS Concept for Attribute ‘agentName’
.
.
.
Attributes
etc.
Object
EVS Concept
for instance objects
Mapping to EVS Concepts
Done at Modeling Time
Semantic Integration:
Metadata Registration Time
ISO11179 mapping
caDSR loading
UML model, including
EVS Concept mappings
Curation:
Data standards registration
for instance data
Semantic Integration: Runtime
Client
HTML/XML
Clients
(Browsers)
SOAP
Clients
Presentation
Web Server
Domain Objects
[Gene, Disease,
Concept,
DataElement]
Tomcat
Servlets
( XML
XSL/XSLT )
RMI
JSPs
Perl
Clients
Object
Object
Managers
SOAP
Data
Access
Objects
(OJB)
Java
Applications
Data
Research
DBs
Research
DBs
caGRID caCORE architecture extension
caGRID Extension (Integration of Discovery and Query Services)
Client
Grid
OGSA-DAI +
Globus
caGRID extension
(Concept Discovery)
caGRID extension
(Federated Query)
OGSA-DAI
caGRID extension
(metadata)
caGRID extension
(query)
Globus
caGRID extension (caBIO adapter)
Data Source
caBIO client
caBIO server
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 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.
responsible for evaluating, developing, and integrating
systems for vocabulary and ontology content, standards,
and software systems for content delivery
CROSS CUTTING WORKSPACE 1
Vocabularies & Common
Data Elements
developing architectural standards and architecture
necessary for other workspaces.
CROSS CUTTING WORKSPACE 2
Architecture
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
Profiles, Targets, Agents, Clinical Trials
biomedical objects
common data elements
controlled vocabulary
NCBI
CGAP
CTEP clinical trials
UCSC
NCI drug
(via DAS)
CGAP gene
KEGG
expression
screening
Gene
Ontologies BioCarta 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