Knowledge Engineering from Experimental Design

‘KEfED’ Gully APC Burns

Information Sciences Institute University of Southern California

The Cycle of Scientific Investigation (‘CoSI’)

Knowledge Engineering from Experimental Design

A typical seminar slide

What is an elemental piece of biomedical scientific knowledge?

For example...

What is an elemental piece of biomedical scientific knowledge?

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 parvicellular paraventricular 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?

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’]

Basic KEfED Elements

Logical Element Icon Activity

Experimental Object

Parameter

Measurement

Branch

Fork

Dependencies between variables are inherent in the experimental protocol

The KEfED Model is intuitive

KEfED handles complex experimental designs

More Below…

Khan et al. (2007), J. Neurosci. 27:7344-60 [expt 2]

KEfED handles complex designs

Khan et al. (2007), J. Neurosci. 27:7344-60 [expt 2]

Example : Neural Connectivity - Observations

‘anterograde’

‘retrograde’

Tract Tracing Experiments Neuroanatomical experiments to study neural connectivity.

injection-site

tracer-chemical

labeling-location

labeling-density

labeling-type

Example : Neural Connectivity - Interpretations

Tract Tracing Experiments > Neuroanatomical Elements Interpretative entities that correspond to facts that may be aggregated into a model

Neuronal Population

cell-bodies

cell-bodies.location

terminal-field.location

terminal-field

‘Neural Connection’

connection-origin

connection-termination

connection-strength

1st look at ‘BioScholar system’: Neural Connectivity Reasoning Tool

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 Powerloom Reasoner)

Computation based on the context of each measurement based on parameters

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]

Using Semantic Web Standards

[https://wiki.birncommunity.org:8443/display/NEWBIRNCC/KEfED+OWL+Model]

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!

Future Directions

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) –  Cartic Ramakrishnan (ISI) –  Marcelo Tallis (ISI) –  Eduard Hovy (ISI)

Other Team members –  Alan Ruttenberg (ScienceCommons) –  Michael Rogan (NYU) –  Gwen Jacobs (MSU) –  Pol Llovet (MSU)

Computer Scientist Contributors –  Hans Chalupsky (ISI) –  Jerry Hobbs (ISI) –  Yolanda Gil (ISI) –  Carl Kesselman (ISI) –  Jose Luis Ambite (ISI)