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W3C Semantic Web for Health Care and Life Sciences Interest Group 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 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 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 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) 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 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? BioRDF: Integrating Heterogeneous Data PDSPki Gene Ontology NeuronDB Reactome BAMS Antibodies Entrez Gene Allen Brain Atlas MESH Literature Mammalian Phenotype SWAN AlzGene BrainPharm Homologene PubChem BioRDF: SPARQL Query BioRDF: Results: Genes, Processes DRD1, 1812 ADRB2, 154 ADRB2, 154 DRD1IP, 50632 DRD1, 1812 DRD2, 1813 GRM7, 2917 GNG3, 2785 GNG12, 55970 DRD2, 1813 ADRB2, 154 CALM3, 808 HTR2A, 3356 DRD1, 1812 SSTR5, 6755 MTNR1A, 4543 CNR2, 1269 HTR6, 3362 GRIK2, 2898 GRIN1, 2902 GRIN2A, 2903 GRIN2B, 2904 ADAM10, 102 GRM7, 2917 LRP1, 4035 ADAM10, 102 ASCL1, 429 HTR2A, 3356 ADRB2, 154 PTPRG, 5793 EPHA4, 2043 NRTN, 4902 CTNND1, 1500 adenylate cyclase activation adenylate cyclase activation arrestin mediated desensitization of G-protein coupled receptor protein signaling pathway dopamine receptor signaling pathway dopamine receptor, adenylate cyclase activating pathway dopamine receptor, adenylate cyclase inhibiting pathway G-protein coupled receptor protein signaling pathway G-protein coupled receptor protein signaling pathway G-protein coupled receptor protein signaling pathway G-protein coupled receptor protein signaling pathway G-protein coupled receptor protein signaling pathway G-protein coupled receptor protein signaling pathway G-protein coupled receptor protein signaling pathway G-protein signaling, coupled to cyclic nucleotide second messenger G-protein signaling, coupled to cyclic nucleotide second messenger G-protein signaling, coupled to cyclic nucleotide second messenger G-protein signaling, coupled to cyclic nucleotide second messenger G-protein signaling, coupled to cyclic nucleotide second messenger glutamate signaling pathway glutamate signaling pathway glutamate signaling pathway glutamate signaling pathway integrin-mediated signaling pathway negative regulation of adenylate cyclase activity negative regulation of Wnt receptor signaling pathway Notch receptor processing Notch signaling pathway serotonin receptor signaling pathway transmembrane receptor protein tyrosine kinase activation (dimerization) ransmembrane receptor protein tyrosine kinase signaling pathway transmembrane receptor protein tyrosine kinase signaling pathway transmembrane receptor protein tyrosine kinase signaling pathway Wnt receptor signaling pathway Many of the genes are related to AD through gamma secretase (presenilin) activity 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 Linked Data 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 Linked Data Browsers Linked Data Mashups Search Engines Thing Thing Thing Thing Thing Thing Thing Thing Thing Thing typed links A typed links B typed links C typed links D E Dereferencing URIs over the Web rdf:type foaf:Person pd:cygri foaf:name 3.405.259 Richard Cyganiak foaf:based_near dp:population dbpedia:Berlin skos:subject skos:subject dp:Cities_in_Germany dbpedia:Hamburg dbpedia:Meunchen skos:subject LODD Data Sets The Linked Data Cloud Translational Medicine Ontology 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 Roles within Translational Medicine Translational Medicine Use Cases Translational Medicine Ontology 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 Scientific Discourse: Overview 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 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 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 Scientific Discourse Workshop http://esw.w3.org/topic/HCLS/ISWC2009/Workshop COI Task Force Task Lead: Vipul Kashap Participants: Eric Prud’hommeaux, Helen Chen, Jyotishman Pathak, Rachel Richesson, Holger Stenzhorn 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? Terminology Task Force Task Lead: John Madden Participants: Chimezie Ogbuji, Helen Chen, Holger Stenzhorn, Mary Kennedy, Xiashu Wang, Rob Frost, Jonathan Borden, Guoqian Jiang 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 subdomain (e.g. pharmacological classification) 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 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