Kefed introduction 12-05-10-2224

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This is an introduction to a knowledge engineering methodology called 'Knowledge Engineering from Experimental Design' (KEfED). This methodology provides a powerful, intuitive method for modeling the …

This is an introduction to a knowledge engineering methodology called 'Knowledge Engineering from Experimental Design' (KEfED). This methodology provides a powerful, intuitive method for modeling the design of scientific experiments and provides the foundation for work at the Biomedical Knowledge Engineering Group at the Information Sciences Institute (run by Gully Burns)

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  • There's a demo online of the web interface for workflow building and putting together a paper.

    There's video on slide 10.

    They're interested in building components for other biomedical applications as well as tools for end users. For instance
    http://yogo.msu.montana.edu/applications/crux.html
    is a graphical editor for KEFED

    Info comes from the literature, external websites, experiments.
    ISI is also working on information integration over web-enabled applications.
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  • 1. Knowledge Engineering from Experimental Design‘KEfED’
    Gully APC Burns
    Information Sciences Institute
    University of Southern California
  • 2. The Cycle of Scientific Investigation (‘CoSI’)
    Knowledge Engineering from Experimental Design
  • 3. A typical seminar slide
    What is an elemental piece of biomedical scientific knowledge?
  • 4. For example...
    What is an elemental piece of biomedical scientific knowledge?
  • 5. The challenge of defining the biomedical semantic web
    Currently consists of a very large number of statements like ‘mice like cheese’
    semantics at this level are complicated!
    For example:
    “Novel neurotrophic factor CDNF protects midbrain dopamine neurons in vivo” [Lindholm et al 2007]
    “Hippocampo-hypothalamic connections: origin in subicular cortex, not ammon's horn.” [Swanson & Cowan 1975]
    “Intravenous 2-deoxy-D-glucose injection rapidly elevates levels of the phosphorylated forms of p44/42 mitogen-activated protein kinases (extracellularly regulated kinases 1/2) in rat hypothalamic parvicellularparaventricular neurons.” [Khan & Watts 2004]
    Statements vary in their levels of reliability, specificity.
    Existing semantic web approaches involve representations of argumentation / claim networks
    Can we invent a new way to introduce formalism?
  • 6. Knowledge Engineering from Experimental Design (‘KEfED’)
    There is an implicit reasoning model employed by scientists to represent their observations based on the way they design experiments
    Standardized experimental templates
    Parameters [‘Independent Variables’]
    Measurements [‘Dependent Variables’]
    Calculations [‘Derived Variables’]
  • 7. Basic KEfED Elements
  • 8. Dependencies between variables are inherent in the experimental protocol
  • 9. The KEfED Model is intuitive
  • 10. KEfED handles complex experimental designs
    Khan et al. (2007), J. Neurosci. 27:7344-60 [expt 2]
    More
    Below…
  • 11. KEfED handles complex designs
    Khan et al. (2007), J. Neurosci. 27:7344-60 [expt 2]
  • 12. ‘anterograde’
    ‘retrograde’
    Example : Neural Connectivity - Observations
    Tract Tracing Experiments
    Neuroanatomical experiments to study neural connectivity.
    labeling-density
    tracer-chemical
    injection-site
    labeling-location
    labeling-type
  • 13. Example : Neural Connectivity - Interpretations
    Tract Tracing Experiments > Neuroanatomical Elements
    Interpretative entities that correspond to facts that may be aggregated into a model
    connection-strength
    ‘Neural Connection’
    connection-termination
    connection-origin
    terminal-field
    Neuronal Population
    cell-bodies.location
    cell-bodies
    terminal-field.location
  • 14. 1st look at ‘BioScholar system’: Neural Connectivity Reasoning Tool
  • 15. Peeking Under the Hood
    ‘PHAL Injection into SUBv generates labeling in MM’ => ‘SUBv contains neurons that project to MM’
    (expressed in First-Order-Logic within PowerloomReasoner)
    Computation based on the context of each measurement based on parameters
  • 16. Crux
    KEfED as the basis for the design of a data repository
    Collaboration with MSU + Science Commons
    Funded by MJFF + Kinetics Foundation to manage data from grantees
    KEfED-editor can as a component in an external web-application
    [http://yogo.msu.montana.edu/applications/crux.html]
  • 17. Using Semantic Web Standards
    [https://wiki.birncommunity.org:8443/display/NEWBIRNCC/KEfED+OWL+Model]
  • 18. OBI
    Use a simplified ‘projection’ with no semantic entailments.
    Seek a simple model with semantics embedded ‘within’ variables
    … work in progress here …
    Seek semantic-web-based links to:
    OBI
    SWAN / SIOC
    ISA-Tab tools
    Domain-specific Reasoning Models (from ‘CoSI’)
    Want to generate hypotheses / predictions that can be expressed as KEfED models?
    $6,000,000 question!
  • 19. Future Directions
  • 20. Acknowledgements
    Funding
    Information Sciences Institute, seed funding
    NIGMS (R01GM083871)
    NIMH (R01MH079068)
    NSF (#0849977)
    Michael J Fox + Kinetics Foundations
    BIRN @ ISI
    Neuroscience Team Members
    Rick Thompson (USC)
    Jessica Turner (MRN)
    Neuroscience Contributors
    Alan Watts (USC)
    Larry Swanson (USC)
    Arshad Khan (USC)
    Computer Scientist Team
    Tom Russ (ISI)
    CarticRamakrishnan (ISI)
    Marcelo Tallis (ISI)
    Eduard Hovy (ISI)
    Other Team members
    Alan Ruttenberg (ScienceCommons)
    Michael Rogan (NYU)
    Gwen Jacobs (MSU)
    PolLlovet (MSU)
    Computer Scientist Contributors
    Hans Chalupsky (ISI)
    Jerry Hobbs (ISI)
    Yolanda Gil (ISI)
    Carl Kesselman (ISI)
    Jose Luis Ambite (ISI)