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Semantic Web Technologies for Translational Medicine

Semantic Web Technologies for Translational Medicine. Vipul Kashyap, PhD vkashyap1@partners.org Senior Medical Informatician, Clinical Knowledge Management and Decision Support Clinical Informatics R&D, Partners Healthcare System Panel on “Towards a Semantic Web for the Life Sciences?”

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Semantic Web Technologies for Translational Medicine

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  1. Semantic Web Technologies for Translational Medicine Vipul Kashyap, PhD vkashyap1@partners.org Senior Medical Informatician, Clinical Knowledge Management and Decision Support Clinical Informatics R&D, Partners Healthcare System Panel on “Towards a Semantic Web for the Life Sciences?” October 24, 2005

  2. Outline • Translational Medicine Use Case • Translation of Genomic Research Insights into Clinical Care • Key Functionalities • Data Integration • Actionable Decision Support • Knowledge Update and Propagation • Semantic Web Technologies • RDF: Resource Description Framework • OWL: Web Ontology Language • SWRL: Semantic Web Rules Language • Conclusions

  3. Translational Medicine Use Case*:Dr. Genomus Meets Basketball Player Who fainted at Practice • Clinical exam reveals abnormal heart sounds • Family History: Father with sudden death at 40, • 2 younger brothers apparently normal • Ultrasound ordered based on clinical exam reveals cardiomyopathy Structured Physical Exam Structured Family History Structured Imaging Study Reports * Use Case provided by Dr. Tonya Hongsermeier

  4. Actionable Decision Support inthe Workflow Context Echo triggers guidance to screen for possible mutations: - MYH7, MYBPC3, TNN2, TNNI3, TPM1, ACTC, MYL2, MYL3

  5. statistics statistics application application server server population ownership registry manager database database encryption Knowledge-based Decision Support Connecting Dx, Rx, Outcomes and Prognosis Data to Genotypic Data for Cardiomyopathy Gene expression in HCM Test Results person raw value concept date Z5937X 3/4 Syncope Outcomes calculated every week microarray (encrypted) Myectomy ER visit Z5937X 3/4 Atrial Arrhythymi Palpitations Z5937X 3/4 ER visits Gene-Chips Z5937X 3/4 Clinic visits Ventricular Arrhy Echocardio Z5937X 4/6 ICD Gene-Chips Z5956X 5/2 Cong. Heart Failure microarray (encrypted) Cardiomyop Z5956X 5/2 Atrial Fib. Z5956X 5/2 Echocardio Z5956X 5/2 EKG Z5956X 3/9 Cardiac Arr Z5956X 3/9 ER Visit Z5956X 3/9 Thalamus Z5956X 3/9

  6. What is RDF? Resource Description Framework – description of any resource Triples <resource, property, value>, e.g., <URI1, “name”, “Mr. X”> Nodes: “URI1”, “Mr. X” Edge: “name” Graph based Data Model RDF graphs are instances of ontological elements What is OWL? Web Ontology Language – description of knowledge and ontologies of a given domain Axioms/constraints capture knowledge about a given domain, e.g., class(Patient), class(Person) Patient  Person Lattice Organization Axioms/constraints are imposed on underlying RDF Graph instances A one slide Introduction to RDF/OWL • URIs (URLs) are used as identifiers for: • Resources, Properties, Values, Namespaces and Ontological Elements • Namespaces contain: • Tags for RDF and OWL languages • Ontological elements (classes, properties) that are instantiated by these RDF Graphs • Ontological elements or XML Schema datatypes that are dimensions of identifiers such as LSIDs

  7. A Strawman Ontology for Translational Medicine OWL ontologies that blend knowledge from the Clinical and Genomic Domains Clinical Knowledge Figure reprinted with permission from Cerebra, Inc. Genomic Knowledge

  8. Data Integration Domain Ontologies for Translational Medicine Instantiation Merged RDF Graph • Use of RDF graphs that instantiate • these ontologies: • - Rules/semantics-based integration • independent of location, method of access or underlying data structures! • Highly configurable, minimize • software coding RDF Graph 1 RDF Graph 2 RDF Wrapper RDF Wrapper EMR Data LIMS Data

  9. Patient (id = URI1) “Mr. X” name has_structured_test_result related_to Patient (id = URI1) Person (id = URI2) MolecularDiagnosticTestResult (id = URI4) associated_relative has_family_history identifies_mutation indicates_disease problem MYH7 missense Ser532Pro (id = URI5) FamilyHistory (id = URI3) “Sudden Death” Dialated Cardiomyopathy (id = URI6) EMR Data LIMS Data Bridging Clinical and Genomic Information “Paternal” 1 90% degree type evidence • Rule/Semantics-based Integration: • Match Nodes with same Ids • Create new links: IF a patient’s structured test result indicates a disease • THEN add a “suffers from link” to that disease

  10. Bridging Clinical and Genomic Information 90% evidence Dialated Cardiomyopathy (id = URI6) “Paternal” suffers_from 1 “Mr. X” type degree name indicates_disease has_structured_test_result related_to Patient (id = URI1) Person (id = URI2) StructuredTestResult (id = URI4) identifies_mutation associated_relative has_family_history has_gene MYH7 missense Ser532Pro (id = URI5) problem FamilyHistory (id = URI3) “Sudden Death” RDF Graphs provide a semantics-rich substrate for decision support. Can be exploited by SWRL Rules

  11. Actionable Decision Support:using SWRL IF the Patient’s structured test result identifies the mutation MYH7 missense:Ser532Pro with confidence ≥ 90% AND the structured test result is indicative of Dialated Cardiomyopathy THEN Patient suffers from Dialated CardioMyopathy Patient has gene MYH7missense:Ser532Pro Perform DCM monitoring and management protocol on the Patient. patient(?p) & molecular_diagnostic_test(?t) & has_structured_test_result(?p, ?t) & identifies_mutation(?t, “MYH7 missense:Ser532Pro”) & indicates_disease(?t, “Dialated Cardiomyopathy”) • suffers_from(?p, “Dialated Cardiomyopathy”)has_gene(?p, “MYH7 missense:Ser532Pro)recommended_intervention(“DCM Monitoring and Management”)

  12. Semantic Web Rules Language (SWRL) • References to ontological concepts and relationships • Describe clinical and genomic information • Can be used to infer patient state: • Patient has a particular gene/mutation • Patient suffers from a particular disease • Can be used to recommend clinical care: • Order Monitoring and Management Protocol patient(?p) & molecular_diagnostic_test(?t) & mutation(?m) & disease(?d) has_structured_test_result(?p, ?t) & identifies_mutation(?t, ?m) & indicates_disease(?t, ?d) & suggested_protocol(?d, ?pro) • suffers_from(?p, ?d)has_gene(?p, ?m)order_protocol(?pro)

  13. Knowledge Update and Propagation IF Molecular Diagnosticreveals MYH7 missense: Ser532Pro or Phe764Leu AND No Structural Heart Disease on Echocardiogram THEN performDCM monitoring and management protocol IF Molecular Diagnostic reveals MYH7 missense: Ser532Pro AND No Structural Heart Disease on Echocardiogram THEN perform late onset of DCM monitoring protocol If Molecular Diagnostic reveals MYH7 missense Phe764LEU AND No Structural Heart Disease on Echocardiogram THEN perform early onset of DCM monitoring protocol • Discovery of New Genotypes • Invention of New Monitoring Protocols • Discovery of Associations between Genotype, Disease and Monitoring Protocols • Modification of Decision Support Rules to Reflect This  Modifies resultant RDF graphs generated! Knowledge Update (Hypothetical)

  14. Knowledge Update and Propagation • Discovery of New Genotypes • Invention of New Monitoring Protocols • Discovery of Associations between Genotype, Disease and Monitoring Protocols • Modification of Decision Support Rules to Reflect This  Modifies resultant RDF graphs generated! IF Molecular Diagnosticreveals MYH7 missense: Ser532Pro or Phe764Leu AND No Structural Heart Disease on Echocardiogram THEN performDCM monitoring and management protocol IF Molecular Diagnostic reveals MYH7 missense: Ser532Pro AND No Structural Heart Disease on Echocardiogram THEN perform late onset of DCM monitoring protocol IF Molecular Diagnostic reveals MYH7 missense Phe764LEU AND No Structural Heart Disease on Echocardiogram THEN perform early onset of DCM monitoring protocol Knowledge Update (Hypothetical)

  15. Knowledge Update and Propagation • Rule • genotype_condition • indicates_disease • recommended_intervention Genotype Disease indicates indicates recommended_intervention Decision Support Logic Update • Use of OWL Inferences for: • Keeping knowledge internally consistent • Propagating changes to Dependent Knowledge • Artifacts Monitoring Protocol • Rule1 • genotype_condition • indicates_disease • recommended_intervention Knowledge Update Genotype2 indicates • Rule2 • genotype_condition • indicates_disease • recommended_intervention Disease Genotype1 recommended_intervention indicates indicates Update Propagation Monitoring Protocol1 Updated RDF Graphs are generated from this point on! Monitoring Protocol2

  16. Conclusions • Translational Medicine is a knowledge intensive field. The ability to capture semantics of this knowledge is crucial for implementation. • Personalized Medicine cannot be implemented in an scalable, efficient and extensible manner without Semantic Web technologies • The rate of Knowledge Updates will change drastically as Genomic knowledge explodes • Automated Semantics-based Knowledge Update and Propagation will be key in keeping the knowledge updated and current

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