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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
Brief Journey Through the
Seven Centers
National Alliance for Medical Image Computing
http://na-mic.org
NIH Roadmap National Centers for Biomedical
Computing (NCBC)
Physics-Based Simulation of
Biological Structures (SIMBIOS)
Russ Altman, PI
National Center for Integrative
Biomedical Informatics (NCIBI)
Brian D. Athey, PI
Informatics for Integrating
Biology and the Bedside (i2b2)
Isaac Kohane, PI
National Alliance for Medical
Imaging Computing (NA-MIC)
Ron Kikinis, PI
The National Center For
Biomedical Ontology (NCBO)
Mark Musen, PI
Multiscale Analysis of Genomic
and Cellular Networks (MAGNet)
Andrea Califano, PI
Center for Computational Biology
(CCB)
Arthur Toga, PI
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/
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.htm
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
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/
National Center for Biomedical Ontology (NCBO)
PI: Mark A. Musen M.D., Ph.D.
PI Institution: Stanford University
Capture and index experimental results
Open
Biomedical
Ontologies
(OBO)
Open
Biomedical
Data (OBD)
Revise
biomedical
understanding
BioPortal
Relate
experimental
data to results
from other
sources
Ontologies are essential to
make sense of biomedical data
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are needed to see this picture.
National Center for Integrative Biomedical Informatics
(NCIBI)
PI: Brian D. Athey Ph.D
PI Institution: University Of Michigan
Core 3
Core 1:
Computational
Technology
Core 2:
Bioinformatics
Technology
Prostate
Cancer
Diabetes
Complications
Core 5: Training
and Education
Core 4: Computing and Data
Infrastructures
Core 6: Outreach and
Dissemination
Core 7: Administrative
Infrastructure
Diabetes
Genetics
Bipolar
Genetics
Granular Overview of NCIBI
Activities Underway: Cores 1-3
First User
Environment
Database
Technologies for
Deep Integration of
Biological
Information
User Environment
Analyses:
Mirel,
Ackerman
labs
A
MiMI Oncomine MBI
MiMI
workflow
BDW
Data
SAGA:
Patel Lab
Bayesian Nets:
Woolf Lab
BDW: Biomedical Data Workbench
prototype: Weymouth
BDW Client
Tools for largeScale analysis
MiMI: Michigan Molecular
Interactions DB:
Jagadish lab, Tarcea
B
C
D
Workflows:
Weymouth,
McEachin, &
Programming team
Track user
actions
MiMI
MBI
MBI: Molecular Biology Integration
DB: States lab, Phillips
Natural Language
Processing: States,
Meng, Radev Labs
Concept Mapper:
Rhodes, Patel,
Woolf
Driving Biological
Problems
Feldman Lab:
Diabetes Type 1,
NRF2 signaling
T1DM pathways
McInnis lab:
Bipolar Disorder
Interactions
Between Genetic
Linkage Peaks,
WNT signaling
Boehnke Lab:
Diabetes Type II
SNP WGA workflow
Genetic Interaction
linkage workflow
Chinnaiyan and
Omenn Labs:
Prostate Cancer
Bayesian, NLP,
Oncomine
Multiscale Analysis of Genomic and
Cellular Networks (MAGNet)
PI: Andrea Califano Ph.D.
PI Institution: Columbia University
• 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.
Core 1: Computational Sciences
Core 2: Bioinformatics
Core 3: Cadherin binding; Cancer; Genetic
determinants of common heritable disorders
(Alzheimer’s, Autism)
Core I, II, III
BISON
MPI
10 U
10 U
Algorithms
Data
Internet
BISON
10 U
Databases
MAGNet/C2B2
BISON
geWorkbench
caGRID
Open Science
Grid
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
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
[email protected]
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…
National Alliance for Medical Image Computing
http://na-mic.org
NIH Staff serving on SDIWG
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)
National Alliance for Medical Image Computing
http://na-mic.org
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)
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/…
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
National Alliance for Medical Image Computing
http://na-mic.org
What would you do?
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)
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 Image Computing
http://na-mic.org
National Alliance for Medical
Imaging Computing
Ron Kikinis, M.D.
Brigham and Women's Hospital
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
National Alliance for Medical Image Computing
http://na-mic.org
Physics-based Simulation of
Biological Structures
Russ Altman, M.D., Ph.D.
Stanford University
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
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
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
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
National Alliance for Medical Image Computing
http://na-mic.org
Center for Computational
Biology
Arthur Toga, Ph.D.
University of California at Los
Angeles
CCB Software Classification
Modeling
Algorithms
Image Processing
Atlas Generation
Cortical Modeling
Registration
Segmentation
Engineering
Open Source
Sequence Annotation
Simulation
SW Development
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
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
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)
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
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)
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
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
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
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 Alliance for Medical Image Computing
http://na-mic.org
National Center for Integrative
Biomedical Informatics
Brian D. Athey, Ph.D.
University of Michigan
NCIBI Software Classification
Database and Information Retrieval Algorithms
Periscope
miBLAST
Workflow Management
GenePattern
Ontologies and Natural Language Processing
MEAD
NewsInEssense
Protégé
Image Processing
Edgewarp
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
National Center for
Protein function pipeline (protein structure) Multi-Scale Study of
Prediction of regulatory (cis) elements
Cellular Networks
Andrea Califano, Ph.D.
REDUCE (gene expression)
Columbia University
MEDUSA (gene expression)
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
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 Alliance for Medical Image Computing
http://na-mic.org
National Center for
Biomedical Ontology
Mark A. Musen, M.D., Ph.D.
Stanford University
NCBO Software Classification
Biomedical Data Annotation
OBO-EDIT
Progammatic Access to Ontologies
Protege Script Tab
Web Access to Ontologies
WebProtege
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.
National Alliance for Medical Image Computing
http://na-mic.org
National Alliance for Medical Image Computing
http://na-mic.org
National Alliance for Medical Image Computing
http://na-mic.org
Contact Peter Lyster
[email protected]
301.594.3928
National Alliance for Medical Image Computing
http://na-mic.org