hcls workshop @ iswc eric neumann and tonya hongsermeier university of georgia nov 6 2006 n.
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HCLS Workshop @ ISWC Eric Neumann and Tonya Hongsermeier University of Georgia, Nov 6, 2006

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HCLS Workshop @ ISWC Eric Neumann and Tonya Hongsermeier University of Georgia, Nov 6, 2006

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  1. HCLS Workshop @ ISWCEric Neumann and Tonya HongsermeierUniversity of Georgia, Nov 6, 2006

  2. W3C Semantic Web for HealthCare and Life Sciences Interest Group • Launched Nov 2005: http://www.w3.org/2001/sw/hcls • Co-chairs: Dr. Tonya Hongsermeier (Partners HealthCare); Eric Neumann (Teranode) • Chartered to develop and support the use of SW technologies and practices to improve collaboration, research and development, and innovation adoption in the of Health Care and Life Science domains • Based on a foundation of semantically rich specifications that support process and information interoperability • HCLS Objectives: • Core vocabularies and ontologies to support cross-community data integration and collaborative efforts • Guidelines and Best Practices for Resource Identification to support integrity and version control • Better integration of Scientific Publication with people, data, software, publications, and clinical trials

  3. HCLS Philosophy • Share use-cases, applications, demonstrations, experiences • Expose collections as RDF using public tools • Develop (where appropriate) core vocabularies for data integration

  4. HCLS Activities • BioRDF - data + NLP as RDF • BioONT - ontology coordination • Adaptive Clinical Protocols and Pathways • Drug Safety and Efficacy • Scientific Publishing - evidence management

  5. Outline • Basic Informatics Challenges • Bench-to-Bedside Applications • What is the Semantic Web? • Current Activities… Case Studies

  6. Drug Discoveryand Medicine • Health • Practice • Safety • Prevention • Privacy • Knowledge Hygieia, G. Klimt

  7. Combine Data Expansion • Large Data SetsVariables >> Samples • Many New Data Types Which Formats?

  8. Where Information Advances are Most Needed • Supporting Innovative Applications in R&D • Translational Medicine (Biomarkers) • Molecular Mechanisms (Systems) • Data Provenance, Rich Annotation • Clinical Information • eHealth Records, EDC, Clinical Submission Documents • Safety Information, Pharmacovigilance, Adverse Events, Biomarker data • Standards • Central Data Sources • Genomics, Diseases, Chemistry, Toxicology • MetaData • Ontologies • Vocabularies

  9. The Big Picture - Hard to understand from just a few Points of View

  10. Complete view tells a very different Story

  11. Distributed Nature of R&D • Silos of Data…

  12. Papers Disease Proteins Genes Retention Policy Assays Compounds Audit Trail Curation Tools Experiment Ontology Data Integration: Biology Requirements

  13. Tox/Efficacy New Regulatory Issues Confronting Pharmaceuticals ADME Optim from Innovation or Stagnation, FDA Report March 2004

  14. Translational Medicine in Drug R&D Early Middle Late Cellular Systems Human In Vitro Studies Animal Studies Clinical Studies Disease Models (Therapeutic Relevance) Toxicities Target/System Efficacy $500K $5M $500M

  15. Translational Research • Improve communication between basic and clinical science so that more therapeutic insights may be derived from new scientific ideas - and vice versa. • Testing of theories emerging from preclinical experimentation on disease-affected human subjects. • Information obtained from preliminary human experimentation can be used to refine our understanding of the biological principles underpinning the heterogeneity of human disease and polymorphism(s). • http://www.translational-medicine.com/info/about • Reference NIH Digital Roadmap activity

  16. HCLS Framework:Biomedical Research • Molecular, Cellular and Systems Biology/Physiology • Organism as an integrated an interacting network of genes, proteins and biochemical reactions • Human body as a system of interacting organs • Molecular Cell Biology/Genomic and Proteomic Research • Gene Sequencing, Genotyping, Protein Structures • Cell Signaling and other Pathways • Biomarker Research • Discovery of genes and gene products that can be used to measure disease progression or impacts of drug • Pharmaco-genomics • Impact of genetic inheritance on • Drug Discovery and Translational Research • Use of preclinical research to identify promising drug candidates

  17. HCLS Framework:Clinical Research • Clinical Trials • Determination of efficacy, impact and safety of drugs for particular diseases • Pharmaco-vigilance/ADE Surveillance • Monitoring of impacts of drugs on patients, especially safety and adverse event related information • Patient Cohort Identification and Management • Identifying patient cohorts for drug trials is a challenging task • Translational Research • Test theories emerging from pre-clinical experimentation on disease affected human subjects • Development of EHRs/EMRs for both clinical research and practice • Currently EHRs/EMRs focussed on clinical workflow processes • Re-using that information for clinical research and trials is a challenging task

  18. Ecosystem: Goal State /* Need to expand this with Biomedical Research + Clinical Practice */ Biomedical Research Clinial Practice /* Need to expand this to include Healthcare and Biomedical Research Players as well… Show an integrated picture with “continuous” information flow */

  19. What is the Semantic Web ? It’s TextExtraction It’s AI It’s SemanticWebs • http://www.w3.org/2006/Talks/0125-hclsig-em/ It’s Web 2.0 It’s DataTracking It’sOntologies It’s a Global Conspiracy

  20. The Current Web What the computer sees: “Dumb” links No semantics - <a href> treated just like <bold> Minimal machine-processable information

  21. The Semantic Web Machine-processable semantic information Semantic context published – making the data more informative to both humans and machines

  22. Property Subject Object <Patient HB2122> <shows_sign> <Disease Pneumococcal_Meningitis> Understanding the Semantic Web • Vision • Some day in the future… • Today-> describing data • Core Concept: TRIPLES… • Specifications • RDF, OWL, GRDDL- • Coming soon: SPARQL, RIF • Applications • Data Aggregation: Recombinant Data • Statements: Annotating things • Practices • Everything gets a URI… • New definition of Data Interoperability: • DTA: Data Transit Authority

  23. Application Space :Semantic Web Drug DD Therapeutics safety Critical Path Chem Lib manufacturing NDA Production Genomics ClinicalStudies HTS eADME Patent Compound Opt DMPK Biology genes informatics

  24. URI - A key element • Uniform Resource Identifier • Specification used in HTML, XML, and RDF-OWL • Fundamental to RDF: It IS the only valid SW identifier! • Two forms: • HTTP- http://biopax.org/pathway/kreb_cycle.owl • URN- urn:lsid:biopax.org:pathway:kreb_cycle • Resolution • Mapping retrievable data to a URI • Does not mean getting everything known about a URI • Not clear how to best handle versioning • See Alan’s slides…

  25. REST-fulness • REST is a term coined by Roy Fielding to describe an architecture style of networked systems. REST is an acronym standing for Representational State Transfer. • http://www.molbio.org/gene (get gene list) • http://www.molbio.org/gene/hugsk3b (get gene info) • Can REST == URI, and if so, when? • Yes, if we agree return function is identical to URI resolution • Issues: • Should it return RDF always? - standardized • Resolution is only a subset of services, how do we handle non-resolution services: are these URI’s as well?

  26. Opportunities for Semantics in HealthCare • Enhanced interoperability via: • Semantic Tagging • Grounding of concepts in Standardized Vocabularies • Complex Definitions • Semantics-based Observation Capture • Inference on Diseases • Phenotypes • Genetics • Mechanisms • Semantics-based Clinical Decision Support • Guided Data Interpretation • Guided Ordering • Semantics-based Knowledge Management

  27. Data Semantics in the Life Sciences Pathways, Biomarkers Publications Complex Objects with Categorical/Taxonomic Data Items Systems Biology Gene expression Publications + data Categorical Taxonomic Data Items Image + Text Data Items Data Items Text Text + data items Composite Objects with Embedded “process” Complex Objects Histology Profiling genomics Clinical Findings Clinical trials Unstructured Data Types Structured and Complex Data Types

  28. RDB => RDF Virtualized RDF

  29. GRDDL XML => RDF (GRDDL) XSL XML RDF

  30. photo2.jpg dc:creator Ben Adida cc:license licenses/by/2.5/ RDFa: Bridging the Hypertext and Semantic Webs • <div xmlns:cc="http://web.resource.org/cc/" xmlns:dc=”http://purl.org/dc/1.1/” about=”photo2.jpg”> • This photo was taken by • <span property=”dc:creator”>Ben Adida</span> • and is licensed under a • <a rel=”cc:license” • href=”http://cc.org/licenses/by/2.5/”> • Creative Commons License • </a>. • </div>

  31. Example:Knowledge Aggregation Courtesy of BG-Medicine

  32. Case Study: Omics ApoA1 … … is produced by the Liver … is expressed less in Atherosclerotic Liver … is correlated with DKK1 … is cited regarding Tangier’s disease … has Tx Reg elements like HNFR1 Subject  Verb  Object

  33. Knowledge Mining using Semantic Web • “Gene Prioritization through Data Fusion” • Aerts et al, 2006, Nature • Use of quantitative and qualitative information for statistical ranking. • Can be used to identify novel genes involved in diseases

  34. DiseaseDescriptions Clinical Obs Applications Mechanisms IRB Molecules Potential Linked Clinical Ontologies SNOMED CDISC ICD10 Clinical Trials ontology RCRIM (HL7) Disease Models Pathways(BioPAX) Tox Genomics Extant ontologies Under development Bridge concept

  35. Case Study: BioPAX (Pathways) • <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> • <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> • <bp:step-interactions> • <bp:MODULATION rdf:ID="xDshToXGSK3b"> • <bp:keft rdf:resource="#xDsh"/> • <bp:right rdf:resource="#xGSK-3beta"/> • <bp:participants rdf:resource="#xGSK-3beta"/> • <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name> • <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > • <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type > • <bp: participants rdf:resource="#xDsh"/> • </bp: MODULATION > • </bp: step-interactions > • </bp: PATHWAYSTEP >

  36. Modulation affectedBy CHIR99102 Case Study: BioPAX (Pathways) • <bp:PATHWAYSTEP rdf:ID="xDshToXGSK3bPathwayStep"> • <bp:next-step rdf:resource="#xGSK3bToBetaCateninPathwayStep"/> • <bp:step-interactions> • <bp:MODULATION rdf:ID="xDshToXGSK3b"> • <bp:keft rdf:resource="#xDsh"/> • <bp:right rdf:resource="#xGSK-3beta"/> • <bp:participants rdf:resource="#xGSK-3beta"/> • <bp:name rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> Dishevelled to GSK3beta</bp:name> • <bp:direction rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> IRREVERSIBLE-LEFT-TO-RIGHT</bp: direction > • <bp:control-type rdf:datatype="http://www.w3.org/2001/XMLSchema#string"> INHIBITION</bp: control-type > • <drug:affectedBy rdf:resource=”http://pharma.com/cmpd/CHIR99102"/> • <bp: participants rdf:resource="#xDsh"/> • </bp: MODULATION > • </bp: step-interactions > • </bp: PATHWAYSTEP >

  37. Case Study: Drug Discovery Dashboards • Dashboards and Project Reports • Next generation browsers for semantic information via Semantic Lenses • Renders OWL-RDF, XML, and HTML documents • Lenses act as information aggregators and logic style-sheets add { ls:TheraTopic hs:classView:TopicView }

  38. Topic: GSK3beta Topic Disease: DiabetesT2 Alt Dis: Alzheimers Target: GSK3beta Cmpd: SB44121 CE: DBP Team: GSK3 Team Person: John Related Set Path: WNT Drug Discovery Dashboard http://www.w3.org/2005/04/swls/BioDash

  39. Bridging Chemistry and Molecular Biology Semantic Lenses: Different Views of the same data BioPax Components Target Model urn:lsid:uniprot.org:uniprot:P49841 Apply Correspondence Rule:if ?target.xref.lsid == ?bpx:prot.xref.lsidthen ?target.correspondsTo.?bpx:prot

  40. Bridging Chemistry and Molecular Biology • Lenses can aggregate, accentuate, or even analyze new result sets • Behind the lens, the data can be persistently stored as RDF-OWL • Correspondence does not need to mean “same descriptive object”, but may mean objects with identical references

  41. Non-synonymous polymorphisms from db-SNP Pathway Polymorphisms • Merge directly onto pathway graph • Identify targets with lowest chance of genetic variance • Predict parts of pathways with highest functional variability • Map genetic influence to potential pathway elements • Select mechanisms of action that are minimally impacted by polymorphisms

  42. BioRDF Neuro Tasks • Aggregate facts and models around Parkinson’s Disease • BIRN / Human Brain Project • SWAN: scientificannotations and evidence • NeuroCommons • Use RDF and OWL to describe • ’Brain Connectivity' • Neuronal data in SenseLab

  43. BioRDF: Reagents • RDF resources that describes various kinds of experimental reagents, starting with antibodies: • Initial RDF that captures: Gene, the fact that this is an antibody, various kinds of pages about the antibody, such as vendor documentation, and any other properties that are explicitly captured in the source material • Work with the Ontology task force to identify appropriate ontologies and vocabularies to use in the RDF. • Write queries against the RDF to answer questions of the sort posed on the Alzforum's

  44. BioRDF: NCBI • NCBI Data: URIs and as RDF (Olivier Bodensreider) • Terminology Integration: NLM’s UMLS, MESH • SNOMED…

  45. Conclusions:Key Semantic Web Principles • Plan for change • Free data from the application that created it • Lower reliance on overly complex Middleware • The value in "as needed" data integration • Big wins come from many little ones • The power of links - network effect • Open-world, open solutions are cost effective • Importance of "Partial Understanding"