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NA-MIC National Alliance for Medical Image Computing http://na-mic.org National Centers for Biomedical Computing Software and Data Integration Working Group (SDIWG) Peter Lyster and Zohara Cohen NA-MIC All Hands Meeting Tuesday January 10, 2006
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National Alliance for Medical Image Computing http://na-mic.org Brief Journey Through the Seven Centers
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NIH Roadmap National Centers for Biomedical Computing (NCBC) Informatics for Integrating Biology and the Bedside (i2b2) Isaac Kohane, PI Center for Computational Biology (CCB) Arthur Toga, PI Multiscale Analysis of Genomic and Cellular Networks (MAGNet) Andrea Califano, PI National Alliance for Medical Imaging Computing (NA-MIC) Ron Kikinis, PI The National Center For Biomedical Ontology (NCBO) Mark Musen, PI Physics-Based Simulation of Biological Structures (SIMBIOS) Russ Altman, PI National Center for Integrative Biomedical Informatics (NCIBI) Brian D. Athey, PI
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Physics-based Simulation of Biological Structures (SIMBIOS) PI: Russ Altman, M.D., Ph.D. PI Institution: Stanford University This Center will develop, disseminate, and support a simulation tool kit (SimTK) that will enable biomedical scientists to develop and share accurate models and simulations of biological structures from atoms to organisms. SimTK will be an open-source, extensible, object-oriented framework for manipulating data, models, and simulations. The software will include advanced capabilities for modeling the geometry and physics of biological systems, generating the governing differential equations of these systems, integrating the equations to simulate the system dynamics, and interpreting the simulation results through comparison with experimental data. http://cbmc-web.stanford.edu/simbios/http://cbmc-web.stanford.edu/simbios/
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National Alliance for Medical Imaging Computing (NAMIC) PI: Ron Kikinis, M.D. PI Institution: Brigham and Women's Hospital The National Alliance for Medical Image Computing (NAMIC), proposed here, will integrate the efforts of leading researchers with a shared vision for development and distribution of the tools required to advance the power of imaging as a methodology for quantifying and analyzing biomedical data. This shared vision is based on a thorough composition of computational methods, from image acquisition to analysis, that builds on the best available practices in algorithm development, software engineering, and application of medical image computing for understanding and mitigating the effects of disease and disability. NAMIC’s goal is to develop, integrate, and deploy computational image analysis systems that are applicable to multiple diseases, in different organs. To provide focus for these efforts, a set of key problems in schizophrenia research has been selected as the initial Driving Biological Projects (DBPs) for NAMIC. http://www.na-mic.org/index.htmhttp://www.na-mic.org/index.htm
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Informatics for Integrating Biology and the Bedside (I2B2) PI: Isaac Kohane, M.D., Ph.D. PI Institution: Brigham and Women's Hospital I2B2 (Informatics for Integrating Biology and the Bedside) is an NIH-funded National Center for Biomedical Computing based at Partners HealthCare System. The I2B2 Center is developing a scalable informatics framework that will bridge clinical research data and the vast data banks arising from basic science research in order to better understand the genetic bases of complex diseases. This knowledge will facilitate the design of targeted therapies for individual patients with diseases having genetic origins. http://www.partners.org/i2b2 http://www.partners.org/i2b2
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Center for Computational Biology (CCB) PI: Arthur Toga, Ph.D. PI Institution: University of California at Los Angeles The Center for Computational Biology (CCB) was established to develop, implement and test computational biology methods that are applicable across spatial scales and biological systems. Our objective is to help elucidate characteristics and relationships that would otherwise be impossible to detect and measure. The CCB employs an integrative approach, both in terms of the biology and the participating disciplines. The Center focuses on the brain, specifically on neuroimaging, and involves research in mathematics, computational methods and informatics. It also is involved in the development of a new form of software infrastructure – the computational atlas – to manage multidimensional data spanning many scales and modalities. This will be specifically applied to the study of brain structure and function in health and disease, but will have much broader applicability to both biomedical computing and computational biology. http://www.loni.ucla.edu/CCB/http://www.loni.ucla.edu/CCB/
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Open Biomedical Ontologies (OBO) Open Biomedical Data (OBD) BioPortal Capture and index experimental results Revise biomedical understanding Relate experimental data to results from other sources National Center for Biomedical Ontology (NCBO) PI: Mark A. Musen M.D., Ph.D. PI Institution: Stanford University
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Ontologies are essential to make sense of biomedical data
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Core 7: Administrative Infrastructure National Center for Integrative Biomedical Informatics (NCIBI) Core 4: Computing and Data Infrastructures Diabetes Genetics Prostate Cancer Bipolar Genetics Diabetes Complications Core 3 Core 1: Computational Technology Core 2: Bioinformatics Technology Core 6: Outreach and Dissemination Core 5: Training and Education PI: Brian D. Athey Ph.D PI Institution: University Of Michigan
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SAGA: Patel Lab Feldman Lab: Diabetes Type 1, NRF2 signaling T1DM pathways Boehnke Lab: Diabetes Type II SNP WGA workflow Genetic Interaction linkage workflow McInnis lab: Bipolar Disorder Interactions Between Genetic Linkage Peaks, WNT signaling Chinnaiyan and Omenn Labs: Prostate Cancer Bayesian, NLP, Oncomine Bayesian Nets: Woolf Lab Workflows: Weymouth, McEachin, & Programming team MBI MiMI MBI: Molecular Biology Integration DB: States lab, Phillips Natural Language Processing: States, Meng, Radev Labs Tools for large- Scale analysis Database Technologies for Deep Integration of Biological Information MiMI: Michigan Molecular Interactions DB: Jagadish lab, Tarcea ABCD MiMI User Environment Analyses: Mirel, Ackerman labs MiMI Onco- mine MBI BDW Data BDW Client Track user actions workflow Concept Mapper: Rhodes, Patel, Woolf BDW: Biomedical Data Workbench prototype: Weymouth First User Environment Driving Biological Problems Granular Overview of NCIBI Activities Underway: Cores 1-3
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Multiscale Analysis of Genomic and Cellular Networks (MAGNet) To study the organization of the complex networks of biochemical interactions whose concerted activity determines cellular processes at increasing levels of granularity. To provide an integrative computational framework to organize molecular interactions in the cell into manageable context dependent components. To develop interoperable computational models and tools that can leverage such cellular interaction maps to elucidate key biological processes and to dissect complex diseases. PI: Andrea Califano Ph.D. PI Institution: Columbia University
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Core 1: Computational Sciences Core 2: Bioinformatics Core 3: Cadherin binding; Cancer; Genetic determinants of common heritable disorders (Alzheimer’s, Autism) MAGNet/C2B2 Core I, II, III Algorithms Data MPI BISON geWorkbench
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National Alliance for Medical Image Computing http://na-mic.org Goals of Software and Data Integration Working Group (SDIWG) The RFA states the goal of creating “the networked national effort to build the computational infrastructure for biomedical computing for the nation”. In furthering this, the goals of the SDIWG in concert with the Project Team and Centers staff are: 1. To advance the domain sciences, and promote software interoperability and data exchange. 2. To capture the collective knowledge of software engineering and practices among the Centers and publish this knowledge widely
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National Alliance for Medical Image Computing http://na-mic.org Mechanics… Google “SDIWG” (on NA-MIC!) Monthly last Friday Breeze/Tcon 2:30 – 3:30 PM ET Open (anyone and open minutes) Chair Peter Lyster lysterp@mail.nih.gov lysterp@mail.nih.gov
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National Alliance for Medical Image Computing http://na-mic.org NCBC Staff serving on SDIWG Bill Lorensen (NA-MIC) Mike Sherman (Simbios) Henry Chueh (I2B2) Ivo Dinov (CCB) Aris Floratos (MAGNet) Daniel Rubin (NCBO) Brian Athey (NCIBI) Many others—Suzi Lewis, David States, Steve Pieper, Tina Kapur, Shawn Murphy, Center PIs…
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National Alliance for Medical Image Computing http://na-mic.org Peter Lyster (NIGMS, Chair) Michael Ackerman (NLM) Carol Bean (NCRR) Art Castle (NIDDK) German Cavelier (NIMH) Larry Clarke (NCI) Zohara Cohen (NIBIB) Elaine Collier (NCRR) Jennifer Couch (NCI) Peter Covitz (NCI) Valentina Di Francesco (NIAID) Dan Gallahan (NCI) Peter Good (NHGRI) John Haller (NIBIB) Donald Harrington (NIBIB) Peter Highnam (NCRR) Michael Huerta (NIMH) Donald Jenkins (NLM) Jennie Larkin (NHLBI) Yuan Liu (NINDS) Michael Marron (NCRR) Richard Morris (NIAID) Bret Peterson (NCRR) Salvatore Sechi (NIDDK) Karen Skinner (NIDA) Michael Twery (NHLBI) Terry Yoo (NLM) NIH Staff serving on SDIWG
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National Alliance for Medical Image Computing http://na-mic.org There are a number of similar efforts to SDIWG at NIH Integrated Cancer Biology Program (ICBP) NIAID (Allergy/Infectious Diseases) Bioinformatics Resource Centers (BRC) Clinical Research Networks (NECTAR) Chronic Disease Outcomes (PROMIS) BIRN/caBIG/Sysbiol/Glue/PSI/TCNP/P41/ PGA/…
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National Alliance for Medical Image Computing http://na-mic.org Discussions in the First Year Demonstration projects Collaborative software development environment (yellow pages, knowledge environment, full development environment or Framework) NIH-forge Emerging efforts in the community (Neuroscience Information Framework, Clearinghouse) Intangibles—pairwise interactions, consciousness raising Example—SimTK ITK
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National Alliance for Medical Image Computing http://na-mic.org What would you do?
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National Alliance for Medical Image Computing http://na-mic.org A Biomedical Software Ontology? ACM categorization Open Directory Project Home grown, tipping point? Why? To help query tools (computer-computer, human-computer) To help describe tools (human-human)
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National Alliance for Medical Image Computing http://na-mic.org NA-MIC Software Classification Applications Slicer2 LONI Pipeline Toolkits Insight Toolkit itk Ontology Visualization Toolkit KWWidgets Software Engineering Tools Testing Dart2 CTest Cross-Platform Build CMake Cross-Platform Distribution CPack Cross-Language Wrapping Cable SWIG National Alliance for Medical Imaging Computing Ron Kikinis, M.D. Brigham and Women's Hospital
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National Alliance for Medical Image Computing http://na-mic.org Simbios Software Classification Deliverable software modules Applications Models Computational components Model-building toolsets Application-building toolsets User and developer documentation Application areas Driving Biological Problems RNA folding Myosin dynamics Cardiovascular fluid dynamics Neuromuscular biomechanics Molecule modeling Electrostatics Physics-based Simulation of Biological Structures Russ Altman, M.D., Ph.D. Stanford University
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National Alliance for Medical Image Computing http://na-mic.org Simbios Software Classification User categories Clinician Experimentalist Application developer Modeler Algorithm developer SimTK software developer Platform support Shared memory multiprocessors Clusters Language support Scripting languages (tcl, perl, python) Java C++, C, Fortran
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National Alliance for Medical Image Computing http://na-mic.org Simbios Software Classification Computation components Linear Algebra (dense, sparse) Numerical Integrators (ODE, DAE, PDE, Stochastic) Monte Carlo simulation Multibody dynamics Contact modeling Meshing PDE solvers Controllers Optimizers/root finders Molecular force field calculations Computational geometry Software development tools Source control Multiplatform build system Regression test support Document generation
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National Alliance for Medical Image Computing http://na-mic.org Simbios Software Classification Software dissemination tools User interaction Bug reporting Feature requests Mailing lists News Software and documentation delivery support Installation Download Upgrade Education Tutorials Course material and software Online courses
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National Alliance for Medical Image Computing http://na-mic.org CCB Software Classification Analysis EDA Feature Analysis Shape Analysis Pattern Recognition Genomic & Phenotypic Data Analysis Statistical Analysis E.g., R Integration DB Efficient DB Traversal & Querying Graphical e.g., HIVE Pipeline Grid Computing Resources Mappers e.g., BrainGraph BrainMapper Portals Resource Integration Components Web Services Center for Computational Biology Arthur Toga, Ph.D. University of California at Los Angeles
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National Alliance for Medical Image Computing http://na-mic.org CCB Software Classification Modeling Algorithms Image Processing Atlas Generation Cortical Modeling Registration Segmentation Engineering Open Source Sequence Annotation Simulation SW Development
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National Alliance for Medical Image Computing http://na-mic.org CCB Software Classification Pre-Processing Data Transforms Spectral Transforms Fourier Transform Wavelet Transform Filtering Skull Stripping Inhomogeneity Correction Visualization Clinical Charts e.g., Demographics Graph Viewers Hyperbolic Graphs Hierarchical Trees Imaging Cross-Sectional Viewers Manifold Viewers 2D, 3D, 4D, ND Sequences
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National Alliance for Medical Image Computing http://na-mic.org I2B2 Software Classification Computer Code Source Application Domain(s) License Tested on platforms Binaries Application Domain(s) License Tested on platforms Informatics for Integrating Biology and the Bedside Isaac Kohane, M.D., Ph.D. Brigham and Women's Hospital
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National Alliance for Medical Image Computing http://na-mic.org I2B2 Software Classification Documentation Biological Experimentation Protocol Algorithm Description Publication PUBMED ID or DOI Human Cohort Description Non-Human Experiment Description Organism Software/Technology protocols Technology white papers Application Domains (Software)
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National Alliance for Medical Image Computing http://na-mic.org I2B2 Software Classification Clinical Information Systems Natural Language Processing Predictive modeling and classification Extract, Transform, and Load Database Schema Ontology Management Service Oriented Architecture Biological Structure Structure:function and structure:disease prediction Protein interaction modeling
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National Alliance for Medical Image Computing http://na-mic.org I2B2 Software Classification Clinical Information Systems (cont.) Expression studies Disease classification/advanced histopathology Outcome prediction Pathway/gene identification for disease Integration of expression w/DNA studies DNA/Genomic studies Population study type (association/linkage etc) Scoring/predicting DNA variant functional and disease significance Comparative genomics Motif studies (TFBS/miRNA/protein domains, etc)
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National Alliance for Medical Image Computing http://na-mic.org I2B2 Software Classification Experimental Data Aggregate Human Data DNA RNA Protein Non-image Phenotype Image Individual Human Data DNA (sequence and mapping) RNA Protein Non-image Phenotype Image Usage Non-human DNA RNA Protein Non-image Phenotype Image
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National Alliance for Medical Image Computing http://na-mic.org I2B2 Software Classification Equipment Genomic Measurement Imaging Imaging Class DNA (sequence and mapping) Gene ID Non-standard descriptor RNA Gene ID Splice variant Non-standard descriptor Protein ID or AA sequence? Non-image Phenotype UMLS or other vocabulary coded Narrative text
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National Alliance for Medical Image Computing http://na-mic.org I2B2 Software Classification Usage Anonymity level Consent level Image DICOM? MIME type? Reagents Chemical Organismal
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National Alliance for Medical Image Computing http://na-mic.org NCIBI Software Classification Data Management TIMBER Michigan Molecular Interactions (MiMI) Biomolecular Interaction Network Database (BIND) Human Proteome Organization Plasma Proteome Project (HUPO) BioWarehouse BioPAX Community and User Interaction Systems Worm Community System MEDSPACE VEHICLES Answer Garden NJFun National Center for Integrative Biomedical Informatics Brian D. Athey, Ph.D. University of Michigan
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National Alliance for Medical Image Computing http://na-mic.org NCIBI Software Classification Database and Information Retrieval Algorithms Periscope miBLAST Workflow Management GenePattern Ontologies and Natural Language Processing MEAD NewsInEssense Protégé Image Processing Edgewarp
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National Alliance for Medical Image Computing http://na-mic.org MAGNet Software Classification Analyses Regulatory/Signaling network reconstruction ARACNE (gene expression) GeneClass (gene expression) Network characterization NetClass/NetBoost (molecular interactions networks) InfoMod (molecular interactions networks) Homology-based protein sequence classification Bio-Kernels (protein sequences) RankProp/MotifProp (protein sequences) Structured-based protein classification Protein function pipeline (protein structure) Prediction of regulatory (cis) elements REDUCE (gene expression) MEDUSA (gene expression) National Center for Multi-Scale Study of Cellular Networks Andrea Califano, Ph.D. Columbia University
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National Alliance for Medical Image Computing http://na-mic.org MAGNet Software Classification Analyses (cont.) Protein structure prediction Protein structure pipeline (protein sequence) META-server (protein sequence) Super-NEST (protein sequence) Protein cavity and binding site prediction (protein structure) Databases Molecular interactions GeneWays Cellular Network KB Gene-phenotype associations PhenotypeML DB
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National Alliance for Medical Image Computing http://na-mic.org NCBO Software Classification Ontology Management Protégé OBO-EDIT Ontology Diff and Alignment PROMPT Ontology Visualization Jambalaya OntoViz PromptViz TGViz National Center for Biomedical Ontology Mark A. Musen, M.D., Ph.D. Stanford University
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National Alliance for Medical Image Computing http://na-mic.org NCBO Software Classification Biomedical Data Annotation OBO-EDIT Progammatic Access to Ontologies Protege Script Tab Web Access to Ontologies WebProtege
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National Alliance for Medical Image Computing http://na-mic.org Suggestion for Next Step 1.Choose one of the existing ontologies as a starting point, perhaps a combination of the MAGNet and CCB ontologies. 2.Find one person at each NCBC who is interested in the issue of categorizing their software and who is willing to work with the SDIWG on incorporating their taxonomy into the chosen top level ontology. 3.The NCBC representatives will incorporate their software into the ontology directly or suggest changes needed in order to incorporate their software. 4.After the software are all integrated into one ontology, a set of attributes to describe the software will be developed. A format for storing the ontology will be agreed upon. 5.Each NCBC will create and host an instance of the ontology to describe their resources. OWL/Semantic Web may be used.
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National Alliance for Medical Image Computing http://na-mic.org
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Contact Peter Lyster lysterp@mail.nih.gov lysterp@mail.nih.gov 301.594.3928
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