Knowledge Engineering

“Knowledge Engineering is an engineering discipline that involves integrating knowledge into computer systems in order to solve complex problems normally requiring a high level of human expertise.”

Feigenbaum & McCorduck 1983

The role of experimental variables

This statistically-significant effect is the experimental basis for the findings of this study.

Focus on experimental variables

from Lindholm, P. et al. (2007), Nature, 448(7149): p. 73-7

‘CDNF protects nigral dopaminergic neurons in-vivo’

An example from Neuroendocrinology

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

Working with the literature

Scientists manually curate data by hand from publications into generic database driven by variable-based model

• Can reuse designs for multiple experiments

• Design process is intuitive, can build a database without informatics training• Ideal for non-computational biologists.• Java / Flex Web application, one click

install

Use Cases

Develop a generic knowledge base for a group (laboratory, database, journal club etc.) driven by curating information from a collection of articles from the literature

‘Neural Connectivity’ Demonstration

Russ TA, Ramakrishnan C, Hovy EH, Bota M, Burns GA. (2011) “Knowledge Engineering Tools for Reasoning with Scientific Observations and Interpretations: a Neural Connectivity Use Case”. BMC Bioinformatics. 12(1):351. [PMID: 21859449]. http://www.biomedcentral.com/1471-2105/12/351/

http://www.bioscholar.org http://code.google.com/p/bioscholar

Working with raw data

• Demonstration system developed for Michael J Fox Foundation + Kinetics in collaboration with MSU + Science Commons

• Framework to help scientists develop intuitive databases for studies of biophysics of drug infusions into the brain.

Use Cases

A generic data-management system for a laboratory based on experimental design.

Developing simpler, intuitive ontologies

- traditional autism = - abnormalities of language - social impairment - perseverative/repetitive behaviors

- Simplify ontologies to focus on restricted set of variables

- What characteristics can we measure?

Autism+:

Use Cases

Computational reasoning systems that characterize disease based on study variables.

‘Neurodevelopment consortium’- William Dobbyns- Alex Paciorkowski - Elliot Scherr

NAVIGATING NIH FUNDINGNIHMaps.org Project Overview - Ned Talley

‘NIHMaps.org’

• Project initiated as academic work, galvanized by NIH program officer, implemented by a commercial company (Chalklabs) and finally hosted by BIRN.

• http://www.nihmaps.org/ • Use large-scale visualization of ~80,000 funded grants per year from NIH using topic-modeling analysis of abstracts and titles + underlying well-structured database.

Use Cases

Use topic modeling and clustering to build models of NIH funding landscape

Users may use interactive system to explore funding patterns, examine related grants, search for competitors and collaborators.

Movies

http://www.nihmaps.org/

Acknowledgements

• Knowledge Engineering Working Group– Gully Burns (ISI) – Jessica Turner (MRN)– Cartic Ramakrishnan (ISI)– Marcelo Tallis (ISI)– (Tom Russ)– (Abhishek Patnia)

• SciKnowMine Collaborators– Ed Hovy (ISI)– Ellen Riloff (ISI) – Karin Verspoor (U Colorado)– Chris Roeder (U Colorado)– Judith Blake (MGI) – ‘Rocky’ Honda (MGI)

• Crux Project Collborators – Michael Rogan (NYU)– Ken Kubota (Kinetics)– Pol Llovet (MSU)– Gwen Jacobs (MSU)– Alan Ruttenberg (Buffalo)

• Sample of Communities– Neurodevelopment consortium– NonHuman Primate Research

Consortium– FBIRN – Stroke Group at MGH – Mouse Genome Informatics – Brain Architecture Group @ USC

BIRN is supported by NCRR and NIH grants 1U24-RR025736, U24-RR021992, U24-RR021760 and by the Collaborative Tools Support Network Award 1U24-RR026057-01. Other funding has also been used : BioScholar (ISI / USC): 5RO1GM083871-04, SciKnowMine: #0849977, and grants from the Michael J Fox and Kinetics Foundation.