Serviceenabling Biomedical Research Enterprise

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Service-enabling Biomedical Research Enterprise

• Chapter 5
• B. Ramamurthy

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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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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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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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BioSem Enterprise Architecture
search
Transform results Ex integrate, generate
metadata
Dissemination Of results
Clinical experiments Ex drug discovery
Diagnostic tools
Clinical data Ex JNI
Research Knowledge Ex Blast
ontology
Academic Knowledge Ex cell, psychology molecular
Treatment methods
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Use case

• Parkinsons 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 Parkinsons 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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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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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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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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Identifying and extracting relationships
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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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PD adding concepts to support information queries
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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
  Huntingtons and Alzeimers. 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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Data sources

• Now answering the question that we posted in
  slide6, three data sources need to be
  integrated
• Neuron database, PDSP KI database, PubChem

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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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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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Generation and merging of RDF graphs
D1 UR14
Parkinsons disease UR16
D_Neuron UR12
Neuron Database
type
associated_with
binds_to
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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Integrated RDF graph
Parkinsons 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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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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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.