1. Parkinson’s Disease Ontology

2. Outline Use Case: Seed Ontology Design Issues
Parkinson’s Disease
Seed Ontology
Design Issues
Extending the seed ontology
Next Steps

3. Use Case: Parkinson’s Disease
Description of Parkinson’s Disease from different perspectives:
Systems Physiology View
Cellular and Molecular Biologist View
Clinical Researcher View
Clinical Guideline Formulator View
Clinical Decision Support Implementer View
Primary Care Clinical View
Neurologist View
Identify Information Needs of the stakeholders identified above
Available at:
Developed by:
Don Doherty
Ken Kawamato

4. Use Case: Systems Physiology View
What chemicals (neurotransmitters) are used by each circuit element (neuron) to communicate with the next element (neuron)? What responses do they elicit in the neurons?

5. Use Case: Cellular and Molecular Biologist View
What proteins are implicated in Parkinson's disease? How are protein expression patterns, protein processing, folding, regulation, transport, protein-protein interactions, protein degradation, etc. affected?

6. Use Case: Clinical Researcher View
Can a certain diagnostic test (e.g., a blood test for a biomarker or an imaging study) provide an approach to diagnosing Parkinson’s disease that is superior to or can complement existing diagnostic approaches?

7. Use Case: Clinical Guideline Formulator View
What have been the results of clinical trials that have evaluated the benefits and costs associated with diagnostic or therapeutic interventions for Parkinson’s disease?

8. Use Case: Clinical Decision Support Implementer View
Which clinical guideline(s) should be used as the basis for implementing the CDS functionality?

9. Use Case: Primary Care Clinician View
If a patient is not currently diagnosed with Parkinson’s disease, do the patient’s current symptoms indicate the need for a referral to a neurologist for further evaluation? If so, what are the referral criteria?

10. Use Case: Neurologist View
What is the differential diagnosis for this patient given his/her symptoms, signs, and diagnostic test results?

11. First Phase Focus on the Cellular and Molecular Biologist View
Develop Parkinson’s Disease Ontology based on that View
Refine it iteratively
Augment it with other views later

12. Parkinson’s Disease Revisited
Studies identifying genes involved with Parkinson's disease are rapidly outpacing the cell biological studies
which would reveal how these gene products are part of the disease process in Parkinson's disease. The
alpha synuclein and Parkin genes are two examples.
The discovery that genetic mutations in the alpha synuclein gene could cause Parkinson's disease in families
has opened new avenues of research in the Parkinson's disease field. When it was also discovered that synuclein
was a major component of Lewy bodies, the pathological hallmark of Parkinson's disease in the brain, it became
clear that synuclein may be important in the pathogenesis of sporadic Parkinson's disease as well as rare cases of
familiar Parkinson's disease. More recently, further evidence for the intrinsic involvement of synuclein in Parkinson's
disease pathogenesis was shown by the finding that the synuclein gene may be triplicated or duplicated in familiar
Parkinson's disease, suggesting that simple overexpression of the wild type protein is sufficient to cause disease.
Since the discovery of synuclein, studies of genetic linkages, specific genes, and their associated coded proteins are
ongoing in the Parkinson's disease research field - transforming what had once been thought of as a purely
environmental disease into one of the most complex multigenetic diseases of the brain.
Studies of genetic linkages, specific genes, and their associated coded proteins are ongoing in the Parkinson's
disease research field. Mutations in the Parkin gene cause early onset Parkinson's disease, and the parkin protein
has been identified as an E3 ligase, suggesting a role for the proteasomal pathway of protein degradation
in Parkinson's disease. DJ-1 and PINK-1 are proteins related to mitochondrial function in neurons, providing an
interesting genetic parallel to mitochondrial toxin studies that suggest disruptions in cellular energetics and oxidative
metabolism are primarily responsible for Parkinson's disease. Other genes, such as UCHL-1, tau, and the
glucocerebrosidase gene, may be genetic risk factors, and their potential role in the sporadic Parkinson's disease
population remains unknown. Mutations in LRRK2, which encodes for a protein called dardarin, is the most recently
discovered genetic cause of Parkinson's disease, and LRRK2 mutations are likely to be the largest cause of familial
Parkinson's disease identified thus far. Dardarin is a large complex protein, which has a variety of structural moieties
that could be participating in more than a dozen different cellular pathways in neurons. Because the cellular pathways
that lead to Parkinson's disease are not fully understood, it is currently unknown, how, or if, any of these pathways intersect in
Parkinson's disease pathogenesis.

13. Step 1: Identify concepts and subsumption hierarchies

14. Step 2: Identify relationships

15. Step 3: Look at Information Queries
What cell signaling pathways
are implicated in the pathogenesis
of Parkinson’s disease?
In which cells?
What proteins are involved
in which pathways?

16. Design Issues: Modeling
Modeling as relationships vs classes
E.g., UHCL-1 transcribed_into Dardarin, vs
Define a class called transcription as follows:
Transcription
has_gene: UHCL-1
has_protein: Dardarin
Modeling a Disease as a dynamic process as opposed to a static class

17. Design Issues: Instance vs SubClass
A generic/specific relationship can be modeled either using instance-of vs subclass-of, for e.g.
Parkinson’s Disease subclassof Disease vs Parkinson’s Disease instance-of Disease
UHCL-1 subclass-of Gene vs UHCL-1 instance-of Gene
Synuclein subclass-of Protein vs Synuclein instance-of Gene
What are the performance impact of these relationships?
Instance-of involves ABox reasoning
Subclass-of involved TBox reasoning
Is one more scalable than the other?
What is the impact on expressivity?
Can “more” knowlledge be represented using one over the other?

18. Design Issue: Granularity
At what level of specificity should relationships be represented in the ontology?
AllelicVariant causes Disease, vs
LRR2KVariant causes Parkinson’s Disease
At what level of genericity should relationships be represented in the ontology?
LewyBody hallmark_of Parkinson’s Disease, vs
AnatomicalEntity hallmark_of Disease

19. Design Issue: Uncertainty
“The discovery that genetic mutations in the alpha synuclein gene could cause Parkinson's disease in families”
The OWL/RDF metamodels do not support expressing this information.
What could be ways of expressing these?
Using reification in RDF?
Introducing new relationships in OWL?
What impact would this have on:
Data Integration?
Reasoning?

20. Design Issue: Domain/Range Polymorphism
What are the semantics of multiple domains and ranges?
Property: associated_with
domain: Pathway
domain: Protein
range: Cell
range: Biomarker
Are RDF/OWL Semantics good enough for us?
Do we need remodel relationships to avoid this?
Different types of polymorphic relationships:
Sub-type polymorphism
Ad-hoc polymorphism

21. Design Issue: Default Values
How do we handle default values of OWL properties
Example:
Default function of proteosomal pathway is protein degradation
What is the impact of default values on biomedical data integration? Reasoning?

22. Design Issue: Ontology Inclusion
Cross-linking to other ontologies such as GO, Neuronames, etc.
If we “link” to a class or property in another ontology:
Should we include associated sub classes?
Should we include associated properties?
Should we include associated axioms?
What if this leads to inconsistencies
Cycles
Contradictions
How does this impact data integration or reasoning?
Can we get by with “shallow” inclusion?

23. Ontology Modularization
Mutually disjoint tree with “cross cutting” properties, axioms, etc.
Proposed by Alan Rector
Example: Different hierarchies/lattices for
Studies (e.g., publication in Pubmed)
Biomedical knowledge referenced in those studies (e.g., association between a gene and a disease)

24. Design Issue: Higher Order Relationships
Example
Association between a Gene and a Disease mentioned in a study

25. Creation of Best Practices
Design issues have been the subject of investigation in the Knowledge Engineering and Medical Informatics communities
Different approaches to resolve these issues will be appropriate in the context of different use cases.
Goal:
Propose various alternatives in the context of use cases proposed in HCLSIG

26. Extending the Seed Ontology
Identify concepts and properties inclusions from:
Gene Ontology
Neuro Names
Decide the “level of inclusion”

27. Extending the Seed ontology
Look at statements from research articles to extend the ontology
Example:
Aggresomes formed by alpha-synuclein and synphilin-1 are cytoprotective.
Create a new property called formedBy
domain(formedBy) = Aggresome
range(formedBy) = Protein
subClassOf(
intersectionOf(Aggresome, Restriction(formedBy, someValuesFrom(intersectionOf(alpha-synuclein, synphilin-1)))), Restriction(function, hasValue(cytoprotective) )

28. Next Steps
Apply this ontology to demonstrate Parkinson’s Disease Use Case
Focus of the BIONT – BIORDF Collaborative F2F