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caBIG : the ca ncer B iomedical I nformatics G rid

caBIG : the ca ncer B iomedical I nformatics G rid. Ken Buetow NCICB/NCI/NIH/DHHS. NCI biomedical informatics.

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caBIG : the ca ncer B iomedical I nformatics G rid

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  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. context • pathways • ontologies components • genes • genotypes • gene expression • proteins • protein expression agents • therapeutics • probes states • Trials • Animal Models 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 • 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] 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 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

  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 Object Data 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. EVS Concept for Class ‘Agent’ EVS Concept for Attribute ‘id’ EVS Concept for Attribute ‘agentName’ . . . etc. Object EVS Concept for instance objects Semantic Integration: Modeling Time Class Attributes Mapping to EVS Concepts Done at Modeling Time

  12. Semantic Integration:Metadata Registration Time ISO11179 mapping caDSR loading UML model, including EVS Concept mappings Curation: Data standards registration for instance data

  13. Semantic Integration: Runtime Client Presentation Data Object HTML/XML Clients (Browsers) Web Server Domain Objects [Gene, Disease, Concept, DataElement] 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

  17. 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

  18. 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

  19. 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

  20. caBIG will facilitate sharing of infrastructure, applications, and data

  21. 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…

  22. 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.

  23. 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

  24. Cancer Molecular Analysis Project (CMAP)- a prototypic biomedical data integration effort biomedical objects common data elements controlled vocabulary 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

  25. Infrastructure Ontologies Databases Applications Clinical trials support Analytic tools Data mining Data Trials Experimental outcomes Genomic Microarray Proteomic caBIG community contributions

  26. 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 acknowledgements • http://ncicb.nci.nih.gov • http://cmap.nci.nih.gov • http://caBIG.nci.nih.gov

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