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Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
Kefed introduction 12-05-10-2224
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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’<br />Gully APC Burns<br />Information Sciences Institute<br />University of Southern California<br />
  • 2. The Cycle of Scientific Investigation (‘CoSI’)<br />Knowledge Engineering from Experimental Design<br />
  • 3. A typical seminar slide<br />What is an elemental piece of biomedical scientific knowledge?<br />
  • 4. For example...<br />What is an elemental piece of biomedical scientific knowledge?<br />
  • 5. The challenge of defining the biomedical semantic web<br />Currently consists of a very large number of statements like ‘mice like cheese’<br />semantics at this level are complicated!<br />For example:<br />“Novel neurotrophic factor CDNF protects midbrain dopamine neurons in vivo” [Lindholm et al 2007]<br />“Hippocampo-hypothalamic connections: origin in subicular cortex, not ammon's horn.” [Swanson & Cowan 1975]<br />“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]<br />Statements vary in their levels of reliability, specificity. <br />Existing semantic web approaches involve representations of argumentation / claim networks<br />Can we invent a new way to introduce formalism? <br />
  • 6. Knowledge Engineering from Experimental Design (‘KEfED’)<br />There is an implicit reasoning model employed by scientists to represent their observations based on the way they design experiments<br />Standardized experimental templates<br />Parameters [‘Independent Variables’] <br />Measurements [‘Dependent Variables’]<br />Calculations [‘Derived Variables’]<br />
  • 7. Basic KEfED Elements<br />
  • 8. Dependencies between variables are inherent in the experimental protocol <br />
  • 9. The KEfED Model is intuitive<br />
  • 10. KEfED handles complex experimental designs<br />Khan et al. (2007), J. Neurosci. 27:7344-60 [expt 2]<br />More <br />Below…<br />
  • 11. KEfED handles complex designs<br />Khan et al. (2007), J. Neurosci. 27:7344-60 [expt 2]<br />
  • 12. ‘anterograde’<br />‘retrograde’<br />Example : Neural Connectivity - Observations<br />Tract Tracing Experiments<br />Neuroanatomical experiments to study neural connectivity.<br />labeling-density<br />tracer-chemical<br />injection-site<br />labeling-location<br />labeling-type<br />
  • 13. Example : Neural Connectivity - Interpretations<br />Tract Tracing Experiments > Neuroanatomical Elements<br />Interpretative entities that correspond to facts that may be aggregated into a model<br />connection-strength<br />‘Neural Connection’<br />connection-termination<br />connection-origin<br />terminal-field<br />Neuronal Population<br />cell-bodies.location<br />cell-bodies<br />terminal-field.location<br />
  • 14. 1st look at ‘BioScholar system’: Neural Connectivity Reasoning Tool<br />
  • 15. Peeking Under the Hood<br />‘PHAL Injection into SUBv generates labeling in MM’ => ‘SUBv contains neurons that project to MM’<br />(expressed in First-Order-Logic within PowerloomReasoner)<br />Computation based on the context of each measurement based on parameters<br />
  • 16. Crux<br />KEfED as the basis for the design of a data repository<br />Collaboration with MSU + Science Commons <br /> Funded by MJFF + Kinetics Foundation to manage data from grantees<br />KEfED-editor can as a component in an external web-application<br />[http://yogo.msu.montana.edu/applications/crux.html]<br />
  • 17. Using Semantic Web Standards<br />[https://wiki.birncommunity.org:8443/display/NEWBIRNCC/KEfED+OWL+Model]<br />
  • 18. OBI<br />Use a simplified ‘projection’ with no semantic entailments.<br />Seek a simple model with semantics embedded ‘within’ variables<br />… work in progress here …<br />Seek semantic-web-based links to:<br />OBI<br />SWAN / SIOC<br />ISA-Tab tools <br />Domain-specific Reasoning Models (from ‘CoSI’)<br />Want to generate hypotheses / predictions that can be expressed as KEfED models?<br />$6,000,000 question!<br />
  • 19. Future Directions<br />
  • 20. Acknowledgements<br />Funding<br />Information Sciences Institute, seed funding <br />NIGMS (R01GM083871)<br />NIMH (R01MH079068)<br />NSF (#0849977) <br />Michael J Fox + Kinetics Foundations <br />BIRN @ ISI<br />Neuroscience Team Members <br />Rick Thompson (USC)<br />Jessica Turner (MRN)<br />Neuroscience Contributors<br />Alan Watts (USC)<br />Larry Swanson (USC)<br />Arshad Khan (USC)<br />Computer Scientist Team <br />Tom Russ (ISI)<br />CarticRamakrishnan (ISI)<br />Marcelo Tallis (ISI)<br />Eduard Hovy (ISI)<br />Other Team members<br />Alan Ruttenberg (ScienceCommons)<br />Michael Rogan (NYU)<br />Gwen Jacobs (MSU)<br />PolLlovet (MSU)<br />Computer Scientist Contributors<br />Hans Chalupsky (ISI)<br />Jerry Hobbs (ISI)<br />Yolanda Gil (ISI)<br />Carl Kesselman (ISI)<br />Jose Luis Ambite (ISI)<br />

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