Semantic Web Technologies for Translational Medicine

1
Semantic Web Technologies for Translational
Medicine

• Vipul Kashyap, PhD
• vkashyap1_at_partners.org
• Senior Medical Informatician, Clinical Knowledge
  Management and Decision Support
• Clinical Informatics RD, Partners Healthcare
  System
• Panel on Towards a Semantic Web for the Life
  Sciences?
• October 24, 2005

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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

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Translational Medicine Use CaseDr. 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
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Actionable Decision Support inthe Workflow
Context
Echo triggers guidance to screen for possible
mutations - MYH7, MYBPC3, TNN2, TNNI3, TPM1,
ACTC, MYL2, MYL3
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Knowledge-based Decision Support

• Connecting Dx, Rx, Outcomes and
• Prognosis Data to Genotypic Data for
  Cardiomyopathy

Gene expression in HCM Test Results
person
concept
date
raw value
Z5937X
3/4
Outcomes calculated every week
Syncope
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
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A one slide Introduction to RDF/OWL

• What is RDF?
• Resource Description Framework description of
  any resource
• Triples ltresource, property, valuegt,
• e.g., ltURI1, name, Mr. Xgt
• 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

• 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

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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
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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
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Bridging Clinical and Genomic Information
Paternal
1
90
type
degree
evidence

• Rule/Semantics-based Integration
• Match Nodes with same Ids
• Create new links IF a patients structured test
  result indicates a disease
• THEN add a
  suffers from link to that disease

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

• IF the Patients structured test result
  identifies the mutation MYH7 missenseSer532Pro
  with confidence 90
• AND the structured test result is indicative of
  Dialated Cardiomyopathy
• THEN
• Patient suffers from Dialated
  CardioMyopathy
• Patient has gene MYH7missenseSer532Pro
• 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 missenseSer532Pro)
  
• indicates_disease(?t, Dialated Cardiomyopathy)
• suffers_from(?p, Dialated Cardiomyopathy)has_ge
  ne(?p, MYH7 missenseSer532Pro)recommended_inter
  vention(DCM Monitoring and Management)

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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)

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Knowledge Update and Propagation

• IF Molecular Diagnostic reveals MYH7 missense
  Ser532Pro or Phe764Leu
• AND No Structural Heart Disease on Echocardiogram
• THEN perform DCM 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

Knowledge Update (Hypothetical)
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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 Diagnostic reveals MYH7 missense
  Ser532Pro or Phe764Leu
• AND No Structural Heart Disease on Echocardiogram
• THEN perform DCM 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)
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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
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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