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)

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)