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Image Ontologies by 7xS3ef

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									    A biological ontology is:
 A machine interpretable representation
  of some aspect of biological reality

    what kinds        eye disc         sense organ
      of things
      exist?       develops                  is_a
                       from
    what are the                 eye
     relationships
     between                        part_of
     these things?
                             ommatidium
 Following basic rules helps
  make better ontologies
 Ontologies must be intelligible both to humans
  (for annotation) and to machines (for reasoning
  and error-checking)
 Unintuitive rules for classification lead to entry
  errors (problematic links)
 Facilitate training of curators
 Overcome obstacles to alignment with other
  ontology and terminology systems
 Enhance harvesting of content through
  automatic reasoning systems
Animal disease models
             Animal models

             Mutant Gene


           Mutant or missing
               Protein


           Mutant Phenotype
     Animal disease models
    Humans            Animal models

  Mutant Gene         Mutant Gene


Mutant or missing   Mutant or missing
    Protein             Protein


Mutant Phenotype    Mutant Phenotype
    (disease)        (disease model)
     Animal disease models
    Humans            Animal models

  Mutant Gene         Mutant Gene


Mutant or missing   Mutant or missing
    Protein             Protein


Mutant Phenotype    Mutant Phenotype
    (disease)        (disease model)
     Animal disease models
    Humans            Animal models

  Mutant Gene         Mutant Gene


Mutant or missing   Mutant or missing
    Protein             Protein


Mutant Phenotype    Mutant Phenotype
    (disease)        (disease model)
SHH-/+   SHH-/-




shh-/+   shh-/-
Phenotype
(clinical sign) = entity   + attribute
Phenotype
(clinical sign) = entity   + attribute
     P1       = eye        + hypoteloric
Phenotype
(clinical sign) = entity   + attribute
     P1       = eye        + hypoteloric
     P2       = midface    + hypoplastic
Phenotype
(clinical sign) = entity   +   attribute
     P1       = eye        +   hypoteloric
     P2       = midface    +   hypoplastic
     P3       = kidney     +   hypertrophied
Phenotype
(clinical sign) = entity   +   attribute
     P1       = eye        +   hypoteloric
     P2       = midface    +   hypoplastic
     P3       = kidney     +   hypertrophied

     ZFIN:                      PATO:
      eye                        hypoteloric
       midface             +     hypoplastic
       kidney                    hypertrophied
Phenotype
(clinical sign) = entity   + attribute


Anatomical ontology
Cell & tissue ontology
Developmental ontology
                           +       PATO
Gene ontology              (phenotype and trait ontology)
  biological process
  molecular function
  cellular component
Phenotype
(clinical sign) = entity   +   attribute
     P1        = eye       +   hypoteloric
     P2        = midface   +   hypoplastic
     P3        = kidney    +   hypertrophied


  Syndrome = P1 + P2 + P3
   (disease)
               = holoprosencephaly
 Human holo-    Zebrafish   Zebrafish
prosencephaly     shh         oep
                   EA model
entity          attribute              attribute
fin             shape                  irregular shape
eye             color hue              blue
mesenchyme      relative thickness     thin
brain           structure              fused
retinal cells   relative orientation   disoriented
           Proposed schema

Association = Genotype Phenotype Environment Assay
Phenotype = Stage* Entity Attribute Value
Entity = OBOClassID
Attribute = PATOVersion2ClassID
    Monadic and relational
         attributes
 Monadic:
    the quality/attribute inheres in a single entity
 Relational:
    the quality/attribute inheres in two or more entities
       sensitivity of an organism to a kind of drug
       sensitivity of an eye to a wavelength of light
    can turn relational attributes into cross-product
     monadic attributes
       e.g. sensitivityToRedLight
       better to use relational attributes
           avoids redundancy with existing ontologies
     Incorporating relational
           attributes

 Association = Genotype Phenotype Environment Assay
 Phenotype = Stage* Entity Attribute Entity*
 Entity = OBOClassID
 Attribute = PATOVersion2ClassID



Example data record:
 Phenotype =
  “organism” sensitiveTo “puromycin”
       Measurable attributes
 Some attributes are inexact and implicitly relative to a
  wild-type or normal attribute
    relatively short, relatively long, relatively reduced
    easier than explicitly representing:
        this tail length shorter-than ‘canonical mouse’ wild-type tail
         length
 Some attributes are determinable
    use a measure function
        unit, value, {time}
    this tail has length L
        measure(L, cm) = 2
 Keep measurements separate from (but linked to)
  attribute ontology
                Incorporating
                measurements

 Association = Genotype Phenotype Environment Assay
 Phenotype = Stage* Entity Attribute Entity* Measurement*
 Measurement = Unit Value (Time)
 Entity = OBOClassID
 Attribute = PATOVersion2ClassID


Example data record:
 Phenotype =
  “gut” “acidic” Measurement = “pH” 5
   Composite phenotype
        classes
 Mammalian phenotype has composite
  phenotype classes
   e.g. “reduced B cell number”
 Compose at annotation time or ontology
  curation time?
   False dichotomy
 Core 2 will help map between composite
  class based annotation and EA
  annotation
  Interpreting annotations
 Annotations are data records
   typically use class IDs
   implicitly refer to instances
 How do we map an annotation to
  instances?
 Important for using annotations
  computationally
Interpreting annotations (1)
 What does an EA (or EAV) annotation mean?
    Annotation:
       Genotype=“FBal00123” E=“brain” A=“fused”
    presumed implied meaning:
       this organism
           has_part x, where
              x instance_of “brain”
              x has_quality “fused”
    or in natural language:
       “this organism has a fused brain”
 Various built-in assumptions
 Interpreting annotations (II)
 What does this mean:
    annotation:
        Genotype=“FBal00123” E=“wing” A=“absent”
    using same mapping as annotation I:
        fly98 has_part x, where
            x instance_of “wing”
            x has_quality “absent”
    or in natural language:
        this fly has a wing which is not there
        !
    What we really intend:
        NOT(this organism has_part x, where x instance_of “wing”)
    Interpreting annotations (II)
   What does this mean:
      annotation:
          Genotype=“FBal00123” E=“wing” A=“absent”
      using same mapping as annotation I:
          this organism has_part x, where
                 x instance_of “wing”
                 x has_quality “absent”
      or in natural language:
          this fly has a wing which is not there
          !
      What we really intend:
          this organism has_quality “wingless”
          “wingless” = the property of having count(has_part “wing”)=0
 Are our computational
  representations intended to capture
  linguistic statements or reality?
         Does this matter?
 Logical reasoners will compute incorrect
  results
   unless explicitly provided with specific rules for
    certain attributes such as “absent”
 What are the consequences?
   Basic search will be fine
      e.g. “find all wing phenotypes”
   But computers will not be able to reason
    correctly
Interpreting annotations (III)
 What does this mean:
   annotation:
      E=“digit” A=“supernumery”
   using same interpretation as annotation I:
      this organism has_part x, where
         x instance_of “digit”
         x has_quality “supernumery”
   or in natural language:
      this organism has a particular finger which is
       supernumery
   What we really intend:
      this person has_quality “supernumery finger”
      “supernumery finger” = the property of having
       count(has_part “digit”) > wild-type”
      !!!
Interpreting annotations (IV)
 What does this mean:
   annotation:
      Gt=“mp001” E=“brown fat cell”
       A=“increased quantity”
   using same mapping as annotation I:
      this organism has_part x, where
         x instance_of “brown fat cell”
         x has_quality “increased quantity”
   or in natural language:
      this organism has a particular brown fat cell which is
       increased in quantity
   What we really intend:
      this organism has_part population_of(“brown fat
       cell”) which has_quality increased size
       Other use cases
 spermatocyte devoid of asters
 Homeotic transformations
 increased distance between wing
  veins
 Some vs all
    Alternate perspectives
 process vs state
   regulatory processes:
      acidification of midgut has_quality reduced rate
      midgut has_quality low acidity
 development vs behavior
   wing development has_quality abnormal
   flight has_quality intermittent
 granularity (scale)
   chemical vs molecular vs cell vs tissue vs
    anatomical part
                    Summary
 Define attributes in terms of instances
 Evaluate proposed new schema
    measurement proposal
    relational attribute proposal
 Complexity trade-off
    create library of use cases
    Core2 will create tools to present user-friendly layer
 Alternate perspective annotations are useful
Before: domain knowledge is
embedded in the db schema
            Gene
            table
                      Exon
                      table

              RNA
             table


            Protein
             table
After: domain knowledge is
embedded in the ontology


   feature
    table
Ontology driven db schema is
 less expensive to maintain
 The logical description and the physical
  database description of the biology are
  developed independently
 Therefore new biological knowledge will
  only require:
     Ontology changes: e.g. new terms
     GUI changes: display
     No schema changes
     No query changes
     No middleware changes
                                Step 1:
                                Build an ontology
                                that reflects reality




Step 2: Data capture
                       Step 3:
                       Classify data
                       using the
     Database:
                       ontology
    UIDs serving
    as proxies for
     instances
Ontologies must adapt over
           time
 Getting it right
    It is impossible to get it
     right the 1st (or 2nd, or
     3rd, …) time.                 Improve
 What we know about
  biology is continually
  growing
 This “standard”                 Collaborate
  requires versioning.
                                  and Learn
           Image Ontologies

              Matthew Fielding
     From RadLex to RadiO
 A unified language for radiology information sources
  (e.g. teaching files, research data, and radiology
  reports).
 Will describe all the salient aspects of an imaging
  examination (e.g., modality, technique, visual features,
  anatomy, and pathology).
 Will emphasize adoption or linkage to established
  terminology and standards when possible, such as the
  ACR Index, SNOMED, the Unified Medical Language
  System (UMLS), the Fleischner Society Glossaries, and
  DICOM.
 Will be used to organize and retrieve radiology images.
           Image Ontologies

             C. Forbes Dewey
                 Experibase
 A common technology that will capture data from all of
  the major experimental systems generating biological
  data.
 Implementing it for gel electrophoresis, microarrays,
  fluorescence-activated cell sorting, mass spectrometry
  and optical microscopy.
 Coordinating with the Interoperable Informatics
  Infrastructure Consortium (I3C)
 Will be used to organize and interrogate these
  experimental data
Image Ontologies

   Bill Lorensen
           Image Ontologies
                   William Bug
           Image Ontology
            Requirements
 Linking databases created at multiple centers
  concerned with human disease and associated animal
  models.
 BIRN Ontology Task Force (OTF) reviews different
  ontological reference interpretations by its audience:
  anatomists, clinicians, genomics, pathologists,
  diagnosticians, and neurologists
 Using existing ontologies, tools, and formalisms wherever
  possible and extend them only as necessary. Any
  ontology work performed by BIRN should be aligned with
  other efforts and provided back to the maintainers
 Developing a set of ontologies that are approved for use
  and a set of policies and procedures for extensions
         Image Ontologies

             Louis Goldberg
On Reasoning with Images
 What different approaches are available for
  spatial, temporal, and spatio-temporal
  representation and reasoning formalisms used
  in computer applications?
 What is the expressive power of those
  formalisms
 Formalizations for commonsense reasoning
  about space and time.
 Formalisms for the representation of vagueness

								
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