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Biomedical Informatics Research Network

Michael Marron, Ph.D., Director Division of Biomedical Technology National Center for Research Resources National Institutes of Health Department of Health and Human Services. Biomedical Informatics Research Network. BIRN Biomedical Informatics Research Network.

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Biomedical Informatics Research Network

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  1. Michael Marron, Ph.D., DirectorDivision of Biomedical TechnologyNational Center for Research ResourcesNational Institutes of HealthDepartment of Health and Human Services Biomedical Informatics Research Network

  2. BIRNBiomedical Informatics Research Network A testbed for a biomedical knowledge infrastructure Federated database of neuroimaging data Fusion of diverse data sources (location; level of aggregation) Grid access to computational resources Datamining software Scalable and extensible Driven by research needs-pull, not technology-push

  3. BIRN Infrastructure • Establish a stable high performance network linking key Biotechnology Centers and General Clinical Research Centers using Internet2 • Establish distributed and linked data collections with partnering groups • Enable the use of "grid-based" computing resources for project specific data refinements and comparisons • Enable data mining from multiple data collections or databases on neuroimaging • Build a stable software and hardware infrastructure that will allow centers to coordinate efforts to accumulate larger studies than can be carried out at one site.

  4. BIRNSites

  5. Information Integration What is the cerebellar distribution of rat proteins with more than 70% homology with human NCS-1? Integrated View Integrated View Definition Mediator Wrapper Wrapper Wrapper Wrapper Web protein localization morphometry neurotransmission CaBP, Expasy

  6. Mouse BIRN • Animal Models of Disease /Multi Scale/Multi Method - MS Mouse and DAT KOM (a schizophrenic and otherwise interesting mouse animal model) • Looking at different resolutions by combining data from multiple modalities • Duke, UCLA, UC San Diego, Cal Tech

  7. Morphology BIRN • Neuroanatomical correlates of neuropsychiatric illness (unipolar depression, Alzheimer's disease, mild cognitive impairment) • Overcome obstacles to the use of neuroimaging data as quantitative outcome measures for clinical investigation including issues raised by longitudinal and multi-site studies. • Do structural differences contribute to specific symptoms such as memory dysfunction or depression independent of diagnosis? • Do specific structural differences distinguish specific diagnostic categories? • Harvard (MGH and BWH), Duke, UCLA, UC San Diego, Johns Hopkins, UC Irvine

  8. Functional BIRN • Characterizing the collection and integration of fMRI data from multiple sites • Study schizophrenia through fMRI images collected from patients around the country using standardized protocols • UCLA, UC San Diego, UC Irvine, Harvard (MGH and BWH), Stanford, Minnesota, Iowa, New Mexico, Duke, North Carolina

  9. NCRR Cisco 4006 DL380 - Network Stats GigE Net Probe N2400 NAS 1.0 TB DL380 Grid POP DL380 APC UPS BIRN Coordinating Center • UC San Diego • Best practices aggregator • Technical development center • Center for training and experience exchange • Expert-level technical support • High-level project management • Oversight bodies point of contact Uniform Hardware for BIRN Sites • Gigabit/10/100 Network Switch – Cisco4006 • Network Statistics System • Gigabit Ethernet Network Probe • Network Attached Storage – Gigabit Ether • 1.0 to 8.0 TB • Grid POP • SRB, Globus • Dual Processor Linux w/ 1GB memory • DL 380 General purpose • Specialized encryption • UPS for Rack

  10. Challenges Large data sets • ISCAR - information storage, curation, archiving, and retrieval • Bandwidth issues • Quality control of data only analyzed by machine • Visual interfaces for complex data sets Fostering collaboration • Reward structures in government and academic institutions • Intellectual property issues • Data integrity • Resistance to collaborate with competitors Data sharing issues (ownership, privacy, authorship)

  11. More Challenges Federated DB /Knowledge Engineering • Dictionaries & Ontologies • Integrating different data types • Accessing legacy systems • Distributed query computations High-level management of projects • Overcome technical & institutional barriers • Degree of centralization needed • Role of standards • Project subdivision and optimization

  12. BIRN Future • Addition of new sites in a rapid, well-defined, affordable fashion • Grid-based National Clinical Research Network • Cross-species integration of data • Characterization of treatment at multiple levels • Expand model to other areas of biomedical research

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