Ontologies, Clinical and Genomic Information How to say what we mean and mean what we say Opportunities

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Ontologies, Clinical and Genomic InformationHow
to say what we mean and mean what we
sayOpportunities Pitfalls

• Alan Rector, Jeremy Rogers, Chris Wroe
• Information Management Group / Bio Health
  Informatics GroupDepartment of Computer Science,
  University of Manchesterrector_at_cs.man.ac.uk
  www.clinical-escience.orgwww.co-ode.orgwww.open
  galen.orgprotege.stanford.org

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What Is An Ontology?

• Ontology (Socrates Aristotle 400-360 BC)
• The study of being
• Word borrowed by computing for the explicit
  description of the conceptualisation of a domain
• concepts (entities)
• properties and attributes of concepts
• constraints on properties and attributes
• Individuals (often, but not always)
• An ontology defines
• a common vocabulary
• a shared understanding
• a classification

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Sharing info ? Sharing meaning

• Metadata
• Data describing the content and meaning of
  resources and services.
• But everyone must speak the same language
• Terminologies
• Shared and common vocabularies
• For search engines, agents, curators, authors and
  users
• But everyone must mean the same thing

• Ontologies
• Shared and common understanding of a domain
• Essential for search, exchange and discovery

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Measure the worldquantitative models(not
ontologies)

• Quantitative
• Numerical data
• 2mm, 2.4V, between 4 and 5 feet
• Unambiguous tokens
• Main problem is accuracy at initial capture
• Numerical analysis (e.g. statistics) well
  understood
• Examples
• How big is this breast lump?
• What is the average age of patients with cancer ?
• How much time elapsed between original referral
  and first appointment at the hospital ?

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describe the the world ontologies

• Qualitative
• Descriptive data
• Cold, colder, blueish, not pink, drunk
• Ambiguous tokens
• Whats wrong with being drunk ?
• Ask a glass of water.
• Accuracy poorly defined
• More examples
• How pleomorphic are the cells in the biopsy?
• What is a proteins function?
• What is the derivation of a tissue?

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Why Develop an Ontology?Naming, Classifying,
Indexing

• To share common understanding of the structure of
  descriptive information
• among people
• among software agents
• between people and software
• To enable reuse of domain knowledge
• to introduce standards to allow interoperability
• To index and annotate other resources

Semantic InteroperabilityFoundation of the
Semantic Web/Grid
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More Reasons

• To make domain assumptions explicit
• easier to change domain assumptions (consider a
  genetics knowledge base)
• easier to understand and update legacy data
• To separate domain knowledge from the operational
  knowledge
• re-use domain and operational knowledge
  separately (e.g., configuration based on
  constraints)
• To manage the combinatorial explosion

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A semantic continuum

• Mike Uschold, Boeing Corp

Shared human consensus
Implicit
? Further to the right ?

• Less ambiguity
• Better inter-operation
• More robust less hardwiring
• More difficult

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An Ontology should be just the Beginning
Databases
Declare structure
Ontologies
Knowledge bases
The SemanticWeb
Provide domain description
Software agents
Problem-solving methods
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What an Ontology Isnt(It wont make the
coffee)

• A database
• Ontologies are about categories/classes/types/conc
  epts/entities not instances
• ABOUT diseases, genes, proteins, ...
  NOT ABOUT specific patients, samples, studies,
  
• A database/EHR schema
• An ontology is about meaning rather than storage
• Although ontology technologies are a means for
  merging schemas
• A decision support/protocol management system
• The entities used in the rules, not the rules
• A metadata schema
• The entities used in the metadata, not the schema
  itself
• A lexicon
• Meaning rather than language
• But every ontology needs language tools

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Ontology Technologies

• Description logics (DLs), OWL
• Designed to provide logical support for automatic
  classification and consistency checking
• Designed for sharing and software engineering
• Leverage off Semantic Web / Grid commnity
• But not everything in OWL is an ontology
• RDF(S)
• Specialised for groups
• DAGEdit and other OBO tools FMA explorer,
• UML
• Carefully developed UML models convey much
  information for an ontology
• But support only very simple inference and
  checking

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Why its hard (1)

• Language is slippery local Rigour logic are
  hard
• Classification is too easy for people (to do
  badly)
• But logical/computational properties unintuitive
• Combinatorial explosions
• Philosophical religious differences
• Information capture
• Data quality
• Tools environments
• Different points of view
• Oncology, Cardiology,
• Adult, developmental, aetiological,
• Clinical, genetic, genomic,

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Why its hard (2)

• Need a combined model of meaning
• The EHR/Database holding the ontology PLUSThe
  ontology held
• Hard to scope easy to do too much
• Just in time ontology
• Better in the bio than the medical community
• Software engineering methods poorly understood

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Classification is easy for people (to do badly)

• On those remote pages it is written that animals
  are divided into
• a. those that belong to the Emperor
• b. embalmed ones
• c. those that are trained
• d. suckling pigs
• e. mermaids
• f. fabulous ones
• g. stray dogs
• h. those that are included in this classification
• i. those that tremble as if they were mad
• j. innumerable ones
• k. those drawn with a very fine camel's hair
  brush
• l. others
• m. those that have just broken a flower vase
• n. those that resemble flies from a distance"

From The Celestial Emporium of Benevolent
Knowledge, Borges
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Avoiding combinatorial explosions

• The Exploding Bicycle From phrase book to
  dictionary grammar
• 1980 - ICD-9 (E826) 8
• 1990 - READ-2 (T30..) 81
• 1995 - READ-3 87
• 1996 - ICD-10 (V10-19 Australian) 587
• V31.22 Injury or accident to the occupant of
  three-wheeled motor vehicle in collision with
  pedal cycle, person on outside of vehicle,
  nontraffic accident, while working for income
• and meanwhile elsewhere in ICD-10
• W65.40 Drowning and submersion while in bath-tub,
  street and highway, while engaged in sports
  activity
• X35.44 Victim of volcanic eruption, street and
  highway, while resting, sleeping, eating or
  engaging in other vital activities

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The ontology nested in the EHR
the ehr (hl7 rim) moodCodeEvent
subjectRelative code


diabetes (subject person_in_family)
the ontology (snomed-ct)
? ltfamily_hx (assoc_find Diabetes)gt
the combined meaning
What is legal? Required? Mandatory?
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Developing Software Engineering Methodologies for
Ontologies

• Building a life cycle
• Use/test cases exemplars
• Identifying problems alternative solutions -
  exploring consequences deciding amongst
  alternatives
• Specifying solutions
• Human and machine readable form
• Setting conformance tests for specifications
• Building reference implementations
• Monitoring for problems
• Recording of problems and changes

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Logic-based Ontologies Conceptual Lego
gene
protein
cell
expression
chronic
acute
bacterial
deletion
polymorphism
ischaemic
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Logic-based Ontologies Conceptual Lego
SNPolymorphism of CFTRGene causing Defect in
MembraneTransport of Chloride Ion causing
Increase in Viscosity of Mucus in CysticFibrosis
Hand which isanatomically normal
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Logical Constructs build complex concepts from
modularisedprimitives
Species
Genes
Function
Disease
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Normalising (untangling) Ontologies
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A simplified example Build a simple treee
easy to maintain
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Let the classifier organise it
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If you want more abstractions,just add new
definitions(re-use existing data)
Diseases linked to abnormal proteins
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And let the classifier work again
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And again even for a quite different category
Diseases linked genes described in the mouse
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Untangling and EnrichmentUsing a classifier to
make life easier
Substance- Protein- - ProteinHormone- - -
Insulin- Steroid- - SteroidHormone- - -
Cortisol- Hormone- -ProteinHormone- - -
Insulin- - SteroidHormone- - - Cortisol-
Catalyst- - Enzyme- - - ATPase
Substance- Protein- - ProteinHormone- - -
Insulin- - Enzyme- - - ATPase- Steroid- -
SteroidHomone- - - Cortisol-Hormone- -
ProteinHormone- - - Insulin- -
SteroidHormone- - - Cortisol- Catalyst- -
Enzyme- - - ATPase
Hormone ? Substance playsRole-someValuesFrom HormoneRole
ProteinHormone ? Protein playsRole someValuesFrom HormoneRole
SteroidHomone ? Steroid playsRole someValuesFrom HormoneRole
Catalyst ? Substance playsRole someValuesFrom CatalystRole
Enzyme ? Protein playsRole someValuesFrom CatalystRole
Insulin ? playsRole someValuesFrom HormoneRole
Cortisol ? playsRole someValuesFrom HormoneRole
ATPase ? playsRole someValuesFrom CatalystRole
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Ontologies and Reference Information Resources

• An ontology is just one part
• Naming - Definitions necessary conditions
• Classification
• Indexing
• Knowledge bases
• What we know about those entities what is true
  in general
• Databases
• What we know about individuals
• Instance stores specialised databases that link
  to ontologies
• Plus
• Lexicons
• Metadata
• Mappings

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Definitionalknowledge
Ontology
Linguistic
Knowledge
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Example 1 Indexing Drug Contraindications(or
guidelines or information or)
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Example 2 Indexing data entry formsFractal
tailoring forms for clinical trials
Hypertension
Hypertension
Idiopathic Hypertension
Idiopathic Hypertension
In our companys studies
In our companys studies
In Phase 2 studies
In Phase 2 studies
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Example 3 PENPADFractal Tailoring of fail
soft forms
What is it sensible to say about ?
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Technical Barriers to linking ontologies

• Overlap
• Linking independent ontologies easyOverlap
  ALWAYS brings differences in meaning
• To integrate, separate
• Appropriate levels of abstraction
• Genetics/Genomics is changing disease
  clqssification
• Anti-angina drugs
• Ingredients conjugated in the liver
• Feedback
• New biology ? new clinical classifications ?
  Disciplin required to keep separations
• Views
• Anatomy Tissues (developmental) vs Structures
  vs Functions

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Nontechnical barriers to linking ontologies

• Organisational barriers
• How to keep separation and scope of individual
  ontologies
• All enterprises tend to expand and encroach
• Discipline barriers
• Task barriers
• Fit for one purpose is not fit for all purposes
• Language barriers
• Between communities as well as languages
• IP barriers
• Process
• Collaborative distributed vs Centralised
• Authority
• Life cycle and rate of change
• GO runs at web speed seconds - days
• SNOMED runs at e-publishing speed 6mo-3 years
• ICD runs at print/committee speed 10-20 years

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Good ontologies

• Fitness for purpose
• Whats it for?
• Defined scope
• Ownership by users
• A language belongs to its community
• Human factors
• Understandability, Reliability!
• Evaluation criteria
• How do we know if it meets its purpose?Evolution

Process not Product!
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Good ontologies

• Internal Structure
• Consistency
• Modularity Normalisation
• Software engineering issues Architecture Tools
• Its software! It evolves! Its a
  standard!Conformance and regression testing
  matter
• Philosophical clarity
• Class-instance divide correct
• Instances are different in ontologies and
  databases
• Ontologies are about a view of the worldNot
  about how to store information in a database
• Clear distinction between part-whole and kind-of

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Grounding cost vs Cleanup cost

• What do we need to share?
• What is broken?
• How much do we need to know to communicate?
• Easy to build too much
• And very costly!
• Just in time ontology
• Use logic
• Use the web
• Bio / OBO does wellMedicine so far doing badly

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Important Ontologies related standards

• OBO (Open Biomedical Ontologies)
• Gene Ontology
• MGED family
• UMLS
• Massive resource for cross referencing
• Use CUIs LUIs Concept Unique IDs Lexical
  Unique IDs
• SNOMED-CT
• SNOMED-International
• Anatomy
• Digital Anatomist FMA, Mouse Developmental, Mouse
  Adult
• SAEL Standard Anatomy Entry List
• NCICB
• CaCORE ontology
• National minimum data sets controlled
  vocabularies
• HL7, LOINC, DICOM, CDISC,
• OpenGALEN source for experimentation and
  development
• Bio databases at least implicit controlled
  vocabularies
• Swissprot, OMIM, , ENSEMBLE, PRINTs,

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Summary Planning forNaming, Classifying,
Indexing

• What is it for? Is there a gap? What is needed?
• What are the use cases? Criteria for success?
• Does it exist already?
• Is an ontology the answer? Is an ontology needed
  for the answer?
• What else is needed?
• A reference knowledge source?
• What is the MINIMUM that one can do?
• Who will own it?
• Can we build it collaboratively?
• What is the authority?
• How will it evolve?
• What is the pace of change?
• Can we do it just in time?
• Can we evaluate and test it again and again?