Download
slide1 n.
Skip this Video
Loading SlideShow in 5 Seconds..
W3C Semantic Web for Health Care and Life Sciences Interest Group PowerPoint Presentation
Download Presentation
W3C Semantic Web for Health Care and Life Sciences Interest Group

W3C Semantic Web for Health Care and Life Sciences Interest Group

166 Vues Download Presentation
Télécharger la présentation

W3C Semantic Web for Health Care and Life Sciences Interest Group

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  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 • Broad industry participation • Over 100 members • Mailing list of over 600 • Background Information • 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. Translating across domains • Translational medicine – use cases that cross domains • Link across domains and research: • What are the links? • gene – transcription factor – protein • pathway – molecular interaction – chemical compound • drug – drug side effect – chemical compound

  5. Challenges • Support of legacy data(bases) • Federated Query • Interface (e.g. support for auto-completion, identifier lookup) • Terminology and Ontology alignment • Large scale reasoning (over large KB) • Modeling hypothetical knowledge

  6. Vision: Concept-based interfaces • The scientist should be able to work in terms of commonly used concepts. • The scientist should be able to work in terms of personal concepts and hypotheses. • - Not be forced to map concepts to the terms that have been chosen for a given application by the application builder.

  7. Interface Sketch:Finding a basis for relation Hypothesis Epigenetic Mechanisms Transcription “There is a relation” Chromatin Transcription Factors Histone Modification Transcription Factor Binding Sites Classes Instances Common Domain position

  8. Biological cartoon as interface KSinBIT’06 Source: Marco Roos

  9. 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

  10. Current 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)

  11. BioRDF Task Force • Task Lead: Kei Cheung • Participants: M. 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

  12. 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

  13. 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? Source: Alan Ruttenberg

  14. 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 Source: Susie Stephens

  15. BioRDF: SPARQL Query Source: Alan Ruttenberg

  16. 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 Source: Alan Ruttenberg

  17. 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

  18. The Classic Web Single information space Built on URIs globally unique IDs retrieval mechanism Built on Hyperlinks are the glue that holds everything together Search Engines Web Browsers HTML HTML HTML hyper-links hyper-links A C B Source: Chris Bizer

  19. 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 Source: Chris Bizer

  20. Data Objects Identified with HTTP URIs rdf:type foaf:Person pd:cygri foaf:name Richard Cyganiak foaf:based_near dbpedia:Berlin pd:cygri = http://richard.cyganiak.de/foaf.rdf#cygridbpedia:Berlin = http://dbpedia.org/resource/Berlin Forms an RDF link between two data sources Source: Chris Bizer

  21. 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 Source: Chris Bizer

  22. 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 Source: Chris Bizer

  23. LODD Data Sets Source: Anja Jentzsch

  24. LODD in Marbles Source: Anja Jentzsch

  25. The Linked Data Cloud Source: Chris Bizer

  26. Pharma Ontology

  27. Participants

  28. Pharma Ontology Deliverables • Review existing ontology landscape • Identify scope of a pharma ontology through understanding employee roles • Identify roughly 30 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 relevant component of ontology for the use case • Build an application that utilizes the ontology

  29. Existing Resources

  30. Roles within Translational Medicine

  31. 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

  32. Scientific Discourse: Overview Source: Tim Clark

  33. 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 Source: Tim Clark

  34. 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 Source: Tim Clark

  35. 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 Source: Tim Clark

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

  37. 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? Source: Holger Stenzhorn

  38. COI: Use Case • Pharmaceutical companies pay a lot to test drugs • Pharmaceutical companies express protocol in CDISC • -- precipitous gap – • Hospitals exchange information in HL7/RIM • Hospitals have relational databases Source: Eric Prud’hommeaux

  39. Inclusion Criteria • Type 2 diabetes on diet and exercise therapy or • monotherapy with metformin, insulin • secretagogue, or alpha-glucosidase inhibitors, or • a low-dose combination of these at 50% • maximal dose. Dosing is stable for 8 weeks prior • to randomization. • … • ?patient takes metformin . Source: Holger Stenzhorn

  40. Exclusion Criteria • Use of warfarin (Coumadin), clopidogrel • (Plavix) or other anticoagulants. • … • ?patient doesNotTake anticoagulant . Source: Holger Stenzhorn

  41. Criteria in SPARQL • ?medication1 sdtm:subject ?patient ;spl:activeIngredient ?ingredient1 . • ?ingredient1 spl:classCode 6809 . #metformin • OPTIONAL { • ?medication2 sdtm:subject ?patient ; spl:activeIngredient ?ingredient2 .?ingredient2 spl:classCode 11289 . #anticoagulant • } FILTER (!BOUND(?medication2)) Source: Holger Stenzhorn

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

  43. Features: the “bridge” to meaning Concepts Features Data Ontology Literature Keyword Vectors Ontology Image(s) Image Features Gene Expression Profile Ontology Microarray Detected Features Ontology Sensor Array

  44. 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)

  45. SKOS & the 80/20 principle: map “down” • Minimal assumptions about expressiveness of source terminology • No assumed formal semantics (no model theory) • Treat it as a knowledge “map” • Extract 80% of the utility without risk of falsifying intent 45 Source: John Madden

  46. The AIDA toolbox for knowledge extraction and knowledge management in a Virtual Laboratory for e-Science

  47. SNOMED CT/SKOS under AIDA: retrieve

  48. Task Force Resources to federate • BioRDF – knowledge base, aTags (stored in KB) • Clinical Observations Interoperability – drug ontology • Linking Open Drug Data – LOD data • Pharma Ontology – ontology • Scientific Discourse – SWAN ontology, SWAN SKOS, myexperiment ontology • Terminology – SNOMED-CT, MeSH, UMLS