Coordinating data interoperability – a W3C perspective

1. Coordinating data interoperability – a W3C perspective M. Scott Marshall, Ph.D.W3C HCLS IG co-chair Leiden University Medical CenterUniversity of Amsterdam http://staff.science.uva.nl/~marshall http://www.w3.org/blog/hcls

2. The Semantic Web is the New Global Web of pre- and post-competitive Knowledge It is about standards for publishing, sharing and querying knowledge drawn from diverse sources It makes it possible to answer sophisticated questions using background knowledge Adapted from: Michel Dumontier

3. Changing tide favors data sharing • Pharmaceutical industry changing strategy from one size fits all to tailored therapy • Personalized Medicine • David Cox (Pfizer) Strategy: Academic / Industry partnership, wellness: rare variants that protect against disease • Biobanking – Data stewards that can facilitate search for rare variants • EHRs

4. Background of the HCLS IG • Originally chartered in 2005 • Chairs: Eric Neumann and Tonya Hongsermeier • Re-chartered in 2008 • Chairs: Scott Marshall and Susie Stephens • Team contact: Eric Prud’hommeaux • Broad industry participation • Over 100 members • Mailing list of over 600 • Background Information • http://www.w3.org/blog/hcls • http://esw.w3.org/topic/HCLSIG

5. Mission of HCLS IG • The mission of HCLS is to develop, advocate for, and support the use of Semantic Web technologies for • Biological science • Translational medicine • Health care • These domains stand to gain tremendous benefit by adoption of Semantic Web technologies, as they depend on the interoperability of information from many domains and processes for efficient decision support

6. Translating across domains EHR Microarray AlzForum PubMed MRI

7. Current Task Forces • BioRDF – federating (neuroscience) knowledge bases • M. Scott Marshall (Leiden University Medical Center / University of Amsterdam) • Clinical Observations Interoperability – patient recruitment in trials • VipulKashyap (Cigna Healthcare) • Linking Open Drug Data – aggregation of Web-based drug data • Susie Stephens (Johnson & Johnson) • Translational Medicine Ontology – high level patient-centric ontology • Michel Dumontier (Carleton University) • Scientific Discourse – building communities through networking • Tim Clark (Harvard University) • Terminology – Semantic Web representation of existing resources • John Madden (Duke University)

8. Some HCLS deliverables • Translational Medicine Ontology • Linked Open Drug Data • CDISC – HL7 ‘light-weight’ bridge for patient eligibility studies • HCLS Knowledge Base • Emerging Best Practices • Expression RDF • Federation

9. BioRDF: Translating across domains EHR Microarray AlzForum PubMed MRI

10. A Bottom-up Approach Community models Provenance models Workflow, experimental design Domain ontologies (DO, GO…) Which genes are markers for neurodegenerative diseases? Provenance of Microarray experiment Was gene ALG2 differentially expressed in multiple experiments? What software was used to analyse the data? Questions How can the experiment be replicated? Results Raw Data Source: Helena Deus

11. LODD: Translating across domains EHR Microarray AlzForum PubMed MRI

12. The Linked Data Cloud Source: Chris Bizer

13. LODD

14. TripleMap

15. Pistoia AllianceVocabulary Services Initiative “The life sciences industry currently operates in an environment where few of the basic components of its study (e.g. genes, proteins, cells, diseases, biomarkers, assays, drugs and technologies) are described using consistent, universally agreed-upon vocabularies.”

16. Homonyms • PSA • Prostate Specific Antigen • PSoriaticArthritis • alpha-2,8-PolySialic Acid • PolySubstanceAbuse • PicrylSulfonicAcid • Polymeric SilicicAcid • Partial Sensory Agnosia • Poultry Science Association Source: MartijnSchuemie

17. Shared Identifiers • Must use common URI’s in order to link data • Provenance related identifiers still needed: • Identifiers for people (researchers) • Identifiers for diseases • Identifiers for terms (Terminology servers) • Identifiers for programs, processes, workflows • Identifiers for chemical compounds • Shared Names http://sharednames.org • Bio2RDF

18. Semagacestat - cancelled • Lilly Alzheimer’s drug cancelled during Phase II trials because it worsened condition of patients • Refresh rate of clinicaltrials.gov ? A pharma consortium could do better.

19. Ideally.. • Pre-competitive data sharing • Share public domain resources such as vocabularies, vocabulary preferences, and literature indexes (Pfizerpedia?) • Share annotated resources • Post-competitive data sharing • Share drug development history info • Drug on the market? Release the clinical trial data! • Share adverse event information

20. Views of linked data • EHR • PMR • Clinical report • Investigator-led Clinical trial • Biobank • Wet lab study

21. Data Sharing Steps • Establish common URI identifiers (PURLs) • PURL server for vocabularies • Create a registry of SPARQL endpoints • Establish query patterns for data discovery i.e. the structure of a federation

22. HCLS IG – how can it fit? • Current HCLS IG is based on volunteer effort – chairs steer but deliverables are determined by fit with day job • Would another type of group fit the goals of the pharma consortia better? • An HCLS task force dedicated to project work for the collaboration

23. Summary • Cross-membership is more effective than occasional liaison calls • Budget time and travel for interaction across the consortia • Budget for contribution to existing projects such as NCBO, SWObjects • Start a collaborative project based on sustainable services

24. The End “Science is built up of facts, as a house is built of stones; but an accumulation of facts is no more a science than a heap of stones is a house.”  – Henri Poincaré, Science and Hypothesis, 1905