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Revolutionizing Data Access: Ontologies for Seamless Knowledge Retrieval in Cardiovascular Research

Explore the implementation of ontologies to simplify obtaining and aligning crucial data for cardiovascular researchers. Learn how ontologies explicitly define existing entities and relationships, facilitating machine-readable knowledge. Discover the potential of Semantic Web technologies for enhanced data interpretation and integration, enabling the utilization of distributed data sources effectively. Dive into the role of frameworks, thesauri, logical constraints, and cataloging in optimizing data access and decision-making processes. Join the discussion on achieving a balance between comprehensibility, accuracy, and the semantic richness of data for groundbreaking research. Experience a demo showcasing the power of Semantic Automated Discovery and Integration in transforming data retrieval and interpretation in the healthcare and life sciences domain.

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Revolutionizing Data Access: Ontologies for Seamless Knowledge Retrieval in Cardiovascular Research

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  1. Carole Goble “Shopping for data should be as easy as shopping for shoes!!”

  2. Web 2.0 + Web 3.0 = Web 5.0? The HSFBCY + CIHR + Microsoft Research SADI and CardioSHARE Projects Mark Wilkinson & Bruce McManus Heart + Lung Institute iCAPTURE Centre, St. Paul’s Hospital, UBC

  3. SPARQL queries and “non-logical” reasoning over distributed data that doesn’t exist

  4. Middleware

  5. Robert Stevens

  6. Shouldn’t beseen!

  7. Good Middlewareenables usefuland excitingbehaviours!

  8. Cool!

  9. For cardiovascularresearchers

  10. The Problem

  11. The Problem

  12. The Holy Grail:(circa 2002) Align the promoters of all serine threonine kinases involved exclusively in the regulation of cell sorting during wound healing in blood vessels. Retrieve and align 2000nt 5' from every serine/threonine kinase in Mus musculus expressed exclusively in the tunica [I | M |A] whose expression increases 5X or more within 5 hours of wounding but is not activated during the normal development of blood vessels, and is <40% homologous in the active site to kinases known to be involved in cell-cycle regulation in any other species.

  13. The Problem

  14. The Problem

  15. The Solution??

  16. Why not? Heart Heart

  17. So… what can we do?

  18. What we need

  19. How do we make knowledge machine-readable?

  20. Ontologies!

  21. Ontology (Gr: “things which exist” +-logy) An explicit formal specification of how to represent the objects, concepts and other entities that are assumed to exist in some area of interest and the relationships that hold among them.

  22. Problem…

  23. Ontology Spectrum Because it fulfils XXX WHY? Frames (Properties) Thesauri “narrower term” relation Selected Logical Constraints (disjointness, inverse, …) Catalog/ ID Formal is-a Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs. Because I say so! 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

  24. Clay Shirky“Ontology is over-rated”http://www.shirky.com/writings/ontology_overrated.html

  25. My Definition of Ontology (for this talk) Ontologies explicitly define the things that exist in “the world” based on what propertieseach kind of thing must have

  26. Ontology Spectrum Frames (Properties) Thesauri “narrower term” relation Selected Logical Constraints (disjointness, inverse, …) Catalog/ ID Formal is-a Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs.

  27. My goal with this talk:the “sweet spot”

  28. COST Frames (Properties) Thesauri “narrower term” relation Selected Logical Constraints (disjointness, inverse, …) Catalog/ ID Formal is-a Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs.

  29. COMPREHENSIBILITY Frames (Properties) Thesauri “narrower term” relation Selected Logical Constraints (disjointness, inverse, …) Catalog/ ID Formal is-a Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs.

  30. Likelihood of being “right” Frames (Properties) Thesauri “narrower term” relation Selected Logical Constraints (disjointness, inverse, …) Catalog/ ID Formal is-a Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs.

  31. Here’s my argument…

  32. Semantic Web? An information system where machines can receive information from one source, re-interpret it, and correctly use it for a purpose that the source had not anticipated.

  33. Semantic Web? If we cannot achieve those two things, then IMO we don’t have a “semantic web”, we only have a distributed (??), linked database… and that isn’t particularly exciting…

  34. Where is the Semantic Web? Frames (Properties) Thesauri “narrower term” relation Selected Logical Constraints (disjointness, inverse, …) Catalog/ ID Formal is-a Informal is-a Formal instance General Logical constraints Terms/ glossary Value Restrs. REASON: “Because I say so” is not open to re-interpretation

  35. If we’re going to build the Semantic Web, we must make Deep Web data a priority from the outset

  36. Founding partner

  37. SADISemantic Automated Discovery and IntegrationBased on the numerous observations we made while watching the usage and behaviour of BioMoby since 2002

  38. SADI Observation #1:Most Web Services in Bioinformatics create implicit biological relationships between their inputs and associated outputs

  39. SADI Observation #1:

  40. SADI PropositionMaking the implicit explicit allows Web Services to be a source of semantically-grounded Triples

  41. SADI PropositionA Web Services registry that provides discovery based on these biological relationships enables the behaviours required of the Semantic Web in Healthcare and Life Science

  42. Dynamic Discovery DistributedInterpretationRe-interpretation

  43. Demo #1 a plug-in to the IO-Informatics Knowledge Explorer

  44. The Sentient Knowledge Explorerfrom IO InformaticsA Semantic Integration, Visualization and Search Tool

  45. SADI Plug-in to the Sentient Knowledge Explorer • The Knowledge Explorer has a plug-ins API that we have utilized to pass data directly from SADI to the KE visualization and exploration system • Data elements in KE are queried for their “type” (rdf:type); this is used to discover additional data properties by querying the SADI Web Service registry

  46. NIST toxicity compendium study • 8 toxicants • Liver • Serum • Urine • Genomic [Affymetrix MA] • Metabolomic [LC/MS] data • conducted under NIST Advanced Technology Program (ATP), Award # 70NANB2H3009 as a Joint Venture between Icoria / Cogenics (Division of CLDA) and IO Informatics. • Microarray studies were conducted under NIEHS contract # N01-ES-65406. • The Alcohol study was conducted under NIAAA contract # HHSN281200510008C in collaboration with Bowles Center for Alcohol Studies (CAS) at UNC. • This work was also made possible through contributions from members of IO Informatics’ Working Group on “Semantic Applications for Translational Research”. BACKGROUND of DATASET

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