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Feasting on Brains! From Web Services to Web 2.0 to the Semantic Web and back again…

Feasting on Brains! From Web Services to Web 2.0 to the Semantic Web and back again… A personal journey through the Semantic Web and Web Services for Health Care and Life Sciences Mark Wilkinson (markw@illuminae.com) Assistant Professor, Medical Genetics University of British Columbia

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Feasting on Brains! From Web Services to Web 2.0 to the Semantic Web and back again…

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  1. Feasting on Brains! From Web Services to Web 2.0 to the Semantic Web and back again… A personal journey through the Semantic Web and Web Services for Health Care and Life Sciences Mark Wilkinson (markw@illuminae.com) Assistant Professor, Medical Genetics University of British Columbia Heart and Lung Research Institute at St. Paul’s Hospital

  2. Benjamin Good(He’s a “Creep”!)

  3. approach “Bioinformatics” is a broad fieldand suffers SEVERE interoperability problems “Bioinformaticians” tend to be specialists in a particular domain of computational analysis As a group, the brains of all bioinformaticians Contain all (known) bioinformatics Is it possible to extract the knowledge Required for interoperability from the brains of bioinformaticians en masse?

  4. “Human Computation” (luis von Ahn)

  5. Ontology Spectrum Thesauri “narrower term” relation Selected Logical Constraints (disjointness, inverse, …) Frames (properties) Formal is-a Catalog/ ID Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs. Originally from AAAI 1999- Ontologies Panel by Gruninger, Lehmann, McGuinness, Uschold, Welty; – updated by McGuinness. Description in: www.ksl.stanford.edu/people/dlm/papers/ontologies-come-of-age-abstract.html

  6. Hair Hair An ontology is a representation of knowledge Animal has Mammal Hair Primate is_a Zombie Lemur Human eats Brains Shoots Chips Classes, instances properties, relationships

  7. Classes Animal Mammal Hair Primate Zombie Lemur Human Brains Shoots Chips

  8. instances

  9. Properties has is_a eats

  10. relations has is_a eats

  11. Hair Hair Hair Hair An ontology is a representation of knowledge Animal has Mammal Hair Primate is_a Zombie Lemur Human eats Classes, instances properties, relationships Brains Shoots Chips

  12. Web Service? A software tool that is accessible over the Web Web Services are intended to be accessed by machines, not people.

  13. Interoperability? The ability of two Web Services to exchange information, and use that information correctly This generally requires Semantics in the form of Ontologies…

  14. Mmmm… Brains!! BioMoby Eating brains to enable Web Service Interoperability

  15. What does BioMoby do?

  16. Create an ontology of bioinformatics data-types • Define an ontology of bioinformatics operations • Open these ontologies for community input • Define Web Services v.v. these two ontologies • AMachine can find an appropriate service • A Machine can execute that service unattended • Ontology is community-extensible The BioMoby Plan

  17. MOBY hosts & services Sequence Express. Protein Alleles … MOBY Central Align Phylogeny Primers Sequence Alignment Gene names Overview of BioMoby Semantic Interoperability

  18. Why couldn’t we do this before?

  19. Interoperability is HARD!

  20. Interoperability throughHuman Computation BioMoby Data Type Ontology: An explicit list of all biological data-types, and the relationships between them. Ontology built, brain by brain, by informaticians! We achieve interoperability simply because informaticians donate their brain-power HUMAN COMPUTATION

  21. A portion of the BioMoby Ontology …built from the brains of the community!

  22. …so what can I do with it?

  23. Analytical workflow Discovery No explicit coordination between providers Run-time discovery of appropriate tools Automated execution of those tools The machine “understands” the data you have in-hand, and assists you in choosing the next step in your analysis.

  24. Interoperability throughHuman Computation Individuals contributed their knowledge about bioinformatics data-types to a central ontology Their combined knowledge enabled the construction of an interoperable framework

  25. Who uses BioMoby?

  26. Usage Statistics 15 Nations > 60 independent institutions >1600 interoperable Bioinformatics Resources ~500,000 requests for “brokering” each month

  27. What have we learned? We can consume the brains of a large community… …to generate something complex, yet organized

  28. Open Kimono The BioMoby ontology is actually quite messy… …communal brains can build useful ontologies, but the problem is…

  29. Ontologies are HARD!

  30. How are ontologies usually constructed?

  31. By small, hard-working, dedicated groups with lots of money! • Gene Ontology & code • Curated: ~5 full-time staff • ~$25 Million(Lewis,S personal communication) • NCI Metathesaurus & code • Curated: ~12 full-time staff • ~$15 Million(Peter K. , estimate) • Health Level 7 (HL7) • Curated • $Lots… Some claim as much as $15 Billion(Smith, Barry, KBB Workshop, Montreal, 2005)

  32. To build the global Semantic Web for Systems Biology we need to encode knowledge from EVERY domain of biology – from barley root apex structure and function, to HIV clinical-trials outcomes… and this knowledge is constantly changing! At >$15M each, can we afford the Semantic Web???

  33. Mmmm… Need MORE Brains!! iCAPTURer experiment

  34. Dr. Bruce McManus with a human heart in his hands He knows his hearts… …but he doesn’tknow how to buildan ontology

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