1. Service-enabling Biomedical Research Enterprise
Chapter 5
B. Ramamurthy
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2. Introduction
Life sciences have witnessed a flurry of innovations triggered by sequencing of human genome as well as genomes of other genomes.
Area of transformational medicine aims to improve communication between basic and clinical science to allow more therapeutic and diagnostic insights.
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3. Translational medicine
From bench to bedside
Exchange ideas, information and knowledge across organizational, governance, socio-cultural, political and national boundaries.
Currently mediated by the internet and exponentially-increasing resources
Digital resources: scientific literature, experimental data, curated annotation (metadata) human and machine generated. Ex: Blast Searches NCBI taxonomy
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4. Driving principles
Key requirements: large volume of data to be managed. How?
Transform to
Digital
Machine readable
Capable of being filtered
Aggregated
Transformed automatically
Context information: use and meaning along with content
Knowledge integration: combines data from research in mouse genetics, cell bilogy, animal neuropsychology, protein biology, neuropathology, and other areas.
Attention to drug discovery, systems bilogy and personalized medicine that rely heavily on integrating and interpreting data produced by experiments.
Heterogenious data
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5. BioSem Enterprise Architecture
Transform results
Ex: integrate,
generate metadata
Dissemination
Of results
Clinical experiments
Ex: drug discovery
Diagnostic tools
Research
Knowledge
Ex: Blast
Clinical data
Ex: JNI
ontology
Academic
Knowledge
Ex: cell,
psychology
molecular
Treatment
methods
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6. Use case Parkinson’s disease (PD): System physiology perspective
Cellular and molecular biology perspective
Pharmacology relating to chemical compounds that bind to receptors
Example query: show me the neuronal components that bind to a ligand which is a therapeutic agent in Parkinson’s disease in reach of the dopaminergic neurons in the substania nigra.
Domain specific shared semantics and classifications
Ontologies can help map among the domains and support seamless integration and interoperation.
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7. Development of Ontologies
Manual interaction between ontologists in experts
Textual descriptions are used for adding to this base
Link pre-existing ontologies for extensive coverage
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8. Ontology design and creation Approach (fig. 5.1)
Subject matter
Knowledge (Text)
Identify core terms
And phrases
Map phrases to
Relationship between
classes
Model terms using ontological
Constructs: classes, properties
Arrange classes and relationships
in subsumption hierarchies
Information
queries
Identify new classes and
relationships
Refine subsumption
hierarchies
Pre-existing classifications
And ontologies
Re-use classes and
relationships
Extenf subsumption
hierarchies
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9. Identifying concepts and hierarchies
Text describing PD in p.105
Study the analysis
Based on the analysis identify important ontological concepts relevant to PD:
Genes
Proteins
Genetic mutations
Diseases
See fig. 5.2
Next step is to identify relationship among concepts
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10. Identifying and extracting relationships
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11. Extending the ontology based on information queries
Consider various queries and identify concepts and relationships needed to be part of PD ontology.
These concepts are needed to retrieve information and knowledge from the system.
This lead to additional new concepts. See fig.5.4
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12. PD: adding concepts to support information queries
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13. Ontology Re-use
It is desirable to re-use the ontology and vocabulary developed in the healthcare and life-sciences fields.
Diseases: PD information can be used in Huntington’s and Alzeimer’s. PD can reuse information from International classification of diseases ICD and its subset SNOMED.
Genes: more genes and genomic concepts such as proteins, pathways are added to ontologies. Consider connecting to Gene Ontology.
Neurological concepts: Consider using Neuro names 2007.
Enzymes: concepts related to enzymes and other chemicals may be required; you may use Enzyme Nomenclature 2007
Be aware of inconsistencies and circularities.
Multiple models may emerge; choice should be based on use cases and functional requirements.
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14. Data sources
Now answering the question that we posted in slide#6, three data sources need to be integrated:
Neuron database, PDSP KI database, PubChem
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15. Data Integration
A centralized approach where data available through web based interfaces is converted into RDF and stored in a centralized repository
A federated approach where data continues to reside in the existing repositories. RDF mediator converts underlying data into RDF format.
RDF allows for focus on logical structures of information in contrast to only representational format (XML) or storage format (relational).
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16. Mapping ontological concepts to RDF graphs
Sample query discussed earlier results in these concepts:
Compartment located_on Neuron
Receptor located_in Compartment
Ligand binds_to Receptor
Ligand associated_with Disease
Next task to map these into RDF maps in the underlying data sources.
Using ontological definitions, data sources, SPARQL queries, and name space, RDF graphs are extracted.
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17. Generation and merging of RDF graphs
UR14
Parkinson’s disease
UR16
D_Neuron
UR12
Neuron Database
type
binds_to
associated_with
Neuron
UR12 D1
UR14
5-H Tryptamine
UR15
5-H Tryptamine
UR15
Located_in
D_Dendrite
UR12
Located_in
PDSPKI Database
PubChem database
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18. Integrated RDF graph 5/1/2019 Parkinson’s disease UR16 D_Neuron UR12
type
associated_with
Neuron
UR12
5-H Tryptamine
UR15
Located_in
binds_to D1
UR14
D_Dendrite
UR12
Located_in
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19. Assignment 2
Consider the PD case study that used ontological approach to querying distributed databases.
Discuss 10 reasons of using this approach as opposed to common SQL query and relational database approach.
Why is Google, Yahoo or MSN search not good enough for searching biological database?
Discuss centralized and federated approach to data integration in the context of this case study.
Submit a softcopy of the document in the digital drop box.
How to do this? Read Chapter 5, read it again. The answers can be formed from the information provided there and from your experience with relational database systems.
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20. Summary
Semantic web technologies provide an attractive technological informatics foundation for enabling the Bench to Bedside Vision.
Many areas of biomedical research including drug discovery, systems biology, personalized medicine rely heavily on integrating and interpreting heterogeneous data set.
This is part of ongoing work in the framework of the work being performed in the Healthcare and Life Sciences Interest Group of W3C.
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