W3C Semantic Web for Health Care and Life Sciences Interest Group

1. W3C Semantic Web for Health Care and Life Sciences Interest Group

2. 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 • 101 formal participants, and mailing list of > 600 • Information about the group • http://www.w3.org/2001/sw/hcls/ • http://esw.w3.org/topic/HCLSIG

3. 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

4. Group Activities • Document use cases to aid individuals in understanding the business and technical benefits of using Semantic Web technologies • Document guidelines to accelerate the adoption of the technology • Implement a selection of the use cases as proof-of-concept demonstrations • Develop high-level vocabularies • Disseminate information about the group’s work at government, industry, and academic events

5. Task Forces • BioRDF – integrated neuroscience knowledge base • Kei Cheung (Yale University) • Clinical Observations Interoperability – patient recruitment in trials • Vipul Kashyap (Cigna Healthcare) • Linking Open Drug Data – aggregation of Web-based drug data • Chris Bizer (Free University Berlin) • Pharma Ontology – high level patient-centric ontology • Christi Denney (Eli Lilly) • Scientific Discourse – building communities through networking • Tim Clark (Harvard University) • Terminology – Semantic Web representation of existing resources • John Madden (Duke University)

6. BioRDF: Answering Questions • Goals: Get answers to questions posed to a body of collective knowledge in an effective way • Knowledge used: Publicly available databases, and text mining • Strategy: Integrate knowledge using careful modeling, exploiting Semantic Web standards and technologies • Participants: Kei Cheung, Scott Marshall, Eric Prud’hommeaux, Susie Stephens, Andrew Su, Steven Larson, Huajun Chen, TN Bhat, Matthias Samwald, Erick Antezana, Rob Frost, Ward Blonde, Holger Stenzhorn, Don Doherty

7. BioRDF: Looking for Targets for Alzheimer’s • Signal transduction pathways are considered to be rich in “druggable” targets • CA1 Pyramidal Neurons are known to be particularly damaged in Alzheimer’s disease • Casting a wide net, can we find candidate genes known to be involved in signal transduction and active in Pyramidal Neurons?

8. BioRDF: Integrating Heterogeneous Data PDSPki NeuronDB Reactome Gene Ontology BAMS Allen Brain Atlas BrainPharm Antibodies Entrez Gene MESH Literature PubChem Mammalian Phenotype SWAN AlzGene Homologene

9. BioRDF: SPARQL Query

10. BioRDF: Results: Genes, Processes • DRD1, 1812 adenylate cyclase activation • ADRB2, 154 adenylate cyclase activation • ADRB2, 154 arrestin mediated desensitization of G-protein coupled receptor protein signaling pathway • DRD1IP, 50632 dopamine receptor signaling pathway • DRD1, 1812 dopamine receptor, adenylate cyclase activating pathway • DRD2, 1813 dopamine receptor, adenylate cyclase inhibiting pathway • GRM7, 2917 G-protein coupled receptor protein signaling pathway • GNG3, 2785 G-protein coupled receptor protein signaling pathway • GNG12, 55970 G-protein coupled receptor protein signaling pathway • DRD2, 1813 G-protein coupled receptor protein signaling pathway • ADRB2, 154 G-protein coupled receptor protein signaling pathway • CALM3, 808 G-protein coupled receptor protein signaling pathway • HTR2A, 3356 G-protein coupled receptor protein signaling pathway • DRD1, 1812 G-protein signaling, coupled to cyclic nucleotide second messenger • SSTR5, 6755 G-protein signaling, coupled to cyclic nucleotide second messenger • MTNR1A, 4543 G-protein signaling, coupled to cyclic nucleotide second messenger • CNR2, 1269 G-protein signaling, coupled to cyclic nucleotide second messenger • HTR6, 3362 G-protein signaling, coupled to cyclic nucleotide second messenger • GRIK2, 2898 glutamate signaling pathway • GRIN1, 2902 glutamate signaling pathway • GRIN2A, 2903 glutamate signaling pathway • GRIN2B, 2904 glutamate signaling pathway • ADAM10, 102 integrin-mediated signaling pathway • GRM7, 2917 negative regulation of adenylate cyclase activity • LRP1, 4035 negative regulation of Wnt receptor signaling pathway • ADAM10, 102 Notch receptor processing • ASCL1, 429 Notch signaling pathway • HTR2A, 3356 serotonin receptor signaling pathway • ADRB2, 154 transmembrane receptor protein tyrosine kinase activation (dimerization) • PTPRG, 5793 ransmembrane receptor protein tyrosine kinase signaling pathway • EPHA4, 2043 transmembrane receptor protein tyrosine kinase signaling pathway • NRTN, 4902 transmembrane receptor protein tyrosine kinase signaling pathway • CTNND1, 1500 Wnt receptor signaling pathway Many of the genes are related to AD through gamma secretase (presenilin) activity

11. Linking Open Drug Data • HCLSIG task started October 1st, 2008 • Primary Objectives • Survey publicly available data sets about drugs • Explore interesting questions from pharma, physicians and patients that could be answered with Linked Data • Publish and interlink these data sets on the Web • Participants: Bosse Andersson, Chris Bizer, Kei Cheung, Don Doherty, Oktie Hassanzadeh, Anja Jentzsch, Scott Marshall, Eric Prud’hommeaux, Matthias Samwald, Susie Stephens, Jun Zhao

12. Linked Data Linked Data Browsers Linked DataMashups Search Engines Thing Thing Thing Thing Thing Thing Thing Thing Thing Thing typedlinks typedlinks typedlinks typedlinks A E C D B • Use Semantic Web technologies to publish structured data on the Web and set links between data from one data source and data from another data sources

13. Dereferencing URIs over the Web 3.405.259 dp:population skos:subject dp:Cities_in_Germany rdf:type foaf:Person pd:cygri foaf:name Richard Cyganiak foaf:based_near dbpedia:Berlin skos:subject dbpedia:Hamburg skos:subject dbpedia:Meunchen

14. LODD Data Sets

15. The Linked Data Cloud

16. Translational Medicine Ontology

17. Deliverables • Review existing ontology landscape • Identify scope of a translational medicine ontology through understanding employee roles • Identify roughly 40 entities and relationships for template ontology • Create 2-3 sketches of use cases (that cover multiple roles) • Select and build out use case (including references to data sets) • Build extensions to the ontology to meet the use case • Build an application that utilizes the ontology

18. Roles within Translational Medicine

19. Translational Medicine Use Cases

20. Translational Medicine Ontology

21. Scientific Discourse Task Force • Task Lead: Tim Clark, John Breslin • Participants: Uldis Bojars, Paolo Ciccarese, Sudeshna Das, Ronan Fox, Tudor Groza, Christoph Lange, Matthias Samwald, Elizabeth Wu, Holger Stenzhorn, Marco Ocana, Kei Cheung, Alexandre Passant

22. Scientific Discourse: Overview

23. Scientific Discourse: Goals • Provide a Semantic Web platform for scientific discourse in biomedicine • Linked to • key concepts, entities and knowledge • Specified • by ontologies • Integrated with • existing software tools • Useful to • Web communities of working scientists

24. Scientific Discourse: Some Parameters • Discourse categories: research questions, scientific assertions or claims, hypotheses, comments and discussion, and evidence • Biomedical categories: genes, proteins, antibodies, animal models, laboratory protocols, biological processes, reagents, disease classifications, user-generated tags, and bibliographic references • Driving biological project: cross-application of discoveries, methods and reagents in stem cell, Alzheimer and Parkinson disease research • Informatics use cases: interoperability of web-based research communities with (a) each other (b) key biomedical ontologies (c) algorithms for bibliographic annotation and text mining (d) key resources

25. Scientific Discourse: SWAN+SIOC • SIOC • Represent activities and contributions of online communities • Integration with blogging, wiki and CMS software • Use of existing ontologies, e.g. FOAF, SKOS, DC • SWAN • Represents scientific discourse (hypotheses, claims, evidence, concepts, entities, citations) • Used to create the SWAN Alzheimer knowledge base • Active beta participation of 144 Alzheimer researchers • Ongoing integration into SCF Drupal toolkit

26. Scientific Discourse Workshop http://esw.w3.org/topic/HCLS/ISWC2009/Workshop

27. COI Task Force • Task Lead: Vipul Kashap • Participants: Eric Prud’hommeaux, Helen Chen, Jyotishman Pathak, Rachel Richesson, Holger Stenzhorn

28. COI: Bridging Bench to Bedside • How can existing Electronic Health Records (EHR) formats be reused for patient recruitment? • Quasi standard formats for clinical data: • HL7/RIM/DCM – healthcare delivery systems • CDISC/SDTM – clinical trial systems • How can we map across these formats? • Can we ask questions in one format when the data is represented in another format?

29. Terminology Task Force • Task Lead: John Madden • Participants: Chimezie Ogbuji, Helen Chen, Holger Stenzhorn, Mary Kennedy, Xiashu Wang, Rob Frost, Jonathan Borden, Guoqian Jiang

30. Terminology: Overview • Goal is to identify use cases and methods for extracting Semantic Web representations from existing, standard medical record terminologies, e.g. UMLS • Methods should be reproducible and, to the extent possible, not lossy • Identify and document issues along the way related to identification schemes, expressiveness of the relevant languages • Initial effort will start with SNOMED-CT and UMLS Semantic Networks and focus on a particular sub-domain (e.g. pharmacological classification)

31. Accomplishments • Technical • HCLS KB hosted at 2 institutes, with content from over 20 data sources • Added many data sources to the Linked Data Cloud • Integration of SWAN and SIOC ontologies for Scientific Discourse • Demonstrator of querying inclusion/exclusion criterion across heterogeneous EHR systems • Outreach • Conference Presentations and Workshops: • Bio-IT World, WWW, ISMB, ISWC, AMIA, Society for Neuroscience, C-SHALS, etc. • Publications: • iTriplification Challenge: Linking Open Drug Data • DILS: Linked Data for Connecting Traditional Chinese Medicine and Western Medicine • ICBO: Pharma Ontology: Creating a Patient-Centric Ontology for Translational Medicine • LOD Workshop, WWW: Enabling Tailored Therapeutics with Linked Data • AMIA Spring Symposium: Clinical Observations Interoperability: A Semantic Web Approach • W3C Note: Semantic Web Applications in Neuromedicine (SWAN) Ontology • W3C Note: SIOC, SIOC Types and Health care and Life Sciences • W3C Note: Alignment Between the SWAN and SIOC Ontologies • W3C Note: A Prototype Knowledge Base for the Life Sciences • W3C Note: Experiences with the Conversion of SenseLab Databases to RDF/OWL • BMC Bioinformatics: Advanced Translational Research with the Semantic Web

32. Conclusions • Early access to use cases and best practice • Influence standard recommendations • Cost effective exploration of new technology through collaboration • Network with others working on the Semantic Web • Group generates resources ranging from papers, use cases, demos, ontologies, and data