Ontologies%20in%20Biomedicine:%20%20The%20Good,%20The%20Bad%20and%20The%20Ugly

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Ontologies in Biomedicine The Good, The Bad
and The Ugly

• Barry Smith
• http//ontology.buffalo.edu/smith

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The Good

• Foundational Model of Anatomy (FMA)
• Pro
• Very clear statement of scope structural human
  anatomy, at all levels of granularity, from the
  whole organism to the biological macromolecule
• Powerful treatment of definitions, from which
  the entire FMA hierarchy is generated can serve
  as basis for formal reasoning
• Con
• Some unfortunate artifacts in the ontology
  deriving from its specific computer
  representation (Protégé)

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Intermediate

• GALEN
• Pro
• Allows formal representation of clinical
  information
• Allows multiple views of relevant detail as
  needed
• Uses powerful Description Logic (DL)-based formal
  structure
• Con
• Remains only partially developed
• Contains errors Vomitus contains carrot
• which DLs did not prevent

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Intermediate

• The Gene Ontology
• Con
• Poor formal architecture
• Full of errors
• menopause part_of death
• Poor support for automatic reasoning and
  error-checking
• Poor treatment of definitions
• Not trans-granular
• No relation to time or instances

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The Gene Ontology

• Pro
• Open Source
• Cross-Species
• ... has recognized the need for reform,
  including explicit representation of granular
  levels

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Problem of Circularity

• GO0042270
• Protection from natural killer cell mediated
  cytolysis
• Definition The process of protecting a cell from
  cytolysis by natural killer cells.

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GO0019836 hemolysis

• Definition The processes that cause hemolysis
• X def. the Y of X
• this is worse than circular

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The Bad

• Reactome
• Pro
• Rich catalogue of biological process
• Con
• Incoherent treatment of categories
• ReferentEntity (embracing e.g. small molecules)
  is a sibling of PhysicalEntity (embracing
  complexes, molecules, ions and particles).
• Similarly CatalystActivity is a sibling of
  Event.

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The Bad

• National Cancer Institute Thesaurus
• Pro
• Open source ambitiously broad coverage
  DL-based
• Con
• Poor realization of DL formalism
• Full of mistakes (many inherited from its UMLS
  sources)
• three disjoint classes of plants Vascular
  Plant, Non-vascular Plant, Other Plant
• three disjoint kinds of cells Cell, Normal
  Cell, Abnormal Cell
• Normal Cell is_a Microanatomy
• See http//ontology.buffalo.edu/medo/NCIT_Smith.ht
  ml

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National Cancer Institute Thesaurus

• Duratec, Lactobutyrin and Stilbene Aldehyde
  classified as Unclassified Drugs and Chemicals
• Pro
• NCIT, too, has recognized the need for reform
• (NCIT is part of the OBO library)

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The UglyUMLS Semantic Network

• Pros
• Broad coverage no multiple inheritance
• Cons
• Incoherent use of conceptual entities
• (e.g. the digestive system as a conceptual part
  of the organism)
• Full of errors

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UMLS Semantic Network

• Edges in the graph represent merely possible
  significant relations
• Bacterium causes Experimental Model of Disease
• Experimental Model of Disease affects Fungus
• Experimental model of disease is_a Pathologic
  Function

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UMLS Semantic Network

• Unclear what the nodes of the graph are
• Drug Delivery Device contains Clinical Drug
• Drug Delivery Device narrower_in_meaning_than
  Manufactured Object
• The use-mention confusion
• Swimming is healthy and has 8 letters

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The UglyClinical Terms Version 2 (The Read
Codes)

• Classifies chemicals into
• chemicals whose name begins with A,
• chemicals whose name begins with B,
• chemicals whose name begins with C, ...

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The Astonishingly (Criminally?) Ugly

• Health Level 7
• HL7 is a UML-based standard for exchange of
  information between clinical information systems
• has proved very crumbly as a standard
• The HL7 Reference Information Model (RIM) is
  supposed to overcome this problem by defining the
  universe of healthcare data in a rigorous way

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HL7-RIM

• Animal
• Definition A subtype of Living Subject
  representing any animal-of-interest to the
  Personnel Management domain.
• Person
• A subtype of Living Subject representing single
  human being sic who, in the context of the
  Personnel Management domain, must also be
  uniquely identifiable through one or more legal
  documents.
• LivingSubject
• Definition A subtype of Entity representing an
  organism or complex animal, alive or not.

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HL7 RIM The Problem of Circularity

• Person Person with documents
• has the form An A is an A which is B
• useless in practical terms since neither we
  nor the machine can use them to find out what A
  means
• incorporate a vicious infinite regress
• have the effect of making it impossible to
  refer to As which are not Bs, for example to an
  undocumented person

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HL7 Logically Incoherent

• act the record of an act
• This has the form An X is the Y of an X
• again worse than circular

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HL7-RIM Logically Contradictory Definitions

• Definition of Act An Act is an action of
  interest that has happened, can happen, is
  happening, is intended to happen, or is
  requested/demanded to happen.
• Definition of Act An Act is the record of
  something that is being done, has been done, can
  be done, or is intended or requested to be done.

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HL7 RIM Ontologically Incoherent

• The truth about the real world is constructed
  through a combination and arbitration of
  attributed statements ...
• As such, there is no distinction between an
  activity and its documentation.

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HL7 Incredibly Successful

• embraced as US federal standard
• central part of 15 billion program to integrate
  all UK hospital information systems
• made mandatory by Canada Health Infoway
• adopted by Oracle as basis for its EHR support
  programs

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HL7 Merchandizing
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From molecules to diseases

• A good ontology should enable us to organize our
  information resources in such a way that we can
  bridge the granularity gap between genomics and
  proteomics data and phenotype (clinical,
  pharmacological, patient-centered) data

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good ontologies require

• Coherent upper level taxonomy distinguishing
• continuants (cells, molecules, organisms ...)
• occurrents (events, processes)
• dependent entities (qualities, functions ...)
• independent entities (their bearers)
• universals (types, kinds)
• instances (tokens, instances)
• Coherent relation ontology supporting inference
  both within and between ontologies.

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good ontologies require

• Consistent use of terms, supported by logically
  coherent (non-circular) definitions, in both
  human-readable and computable formats

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Open Biomedical Ontologies (OBO) Upper Biomedical
Ontology (UBO)

• root UBO0000001top
• subclass BFOcontinuantcontinuant
• subclass BFOdependent_entitydependent_entity
• subclass UBO0000023quality
• subclass UBO0000026phenotype
• subclass UBO0000025state
• subclass UBO0000027disease
• subclass UBO0000005function
• subclass GO0003674molecular_function
• subclass BFOdispositiondisposition
• subclass BFOindependent_entityindependent_enti
  ty
• subclass UBO0000002substance
• subclass UBO0000019protein
• subclass GO0005575cellular_component
• subclass UBO0000006anatomical_entity
• subclass UBO0000008gross_anatomical_entity
• subclass UBO0000007organism
• subclass UBO0000015microbe
• subclass UBO0000014plant

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OBO Relation Ontology (RO)

• Clear distinction between universals (classes,
  kinds, types and instances (individuals, tokens
• Precise formal definitions of relations
• Automatic applicability to time-indexed
  instance-data e.g. in Electronic Health Record
• Consistency with the Relation Ontology now a
  criterion for admission to the OBO ontology
  library
• see Genome Biology Apr. 2006

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Three types of relations

• between instances
• Marys heart part_of Mary
• between an instance and a universal
• Mary instance_of homo sapiens
• between universals
• gastrulation part_of embryonic development

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A suite of primitive instance-level relations

• identical_to
• part_of
• located_in
• adjacent_to
• earlier
• derives_from
• ...

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A suite of defined relations between universals
Foundational is_apart_of
Spatial located_incontained_inadjacent_to
Temporal transformation_ofderives_frompreceded_by
Participation has_participanthas_agent
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GALEN Vomitus contains carrot

• All portions of vomit contain all portions of
  carrot
• All portions of vomit contain some portion of
  carrot
• Some portions of vomit contain some portion of
  carrot
• Some portions of vomit contain all portions of
  carrot

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• all-some structure
• A part_of B def. given any instance a of A there
  is some instance b of B such that a part_of b on
  the instance level
• Allows automatic ontology integration via
  cascading reasoning
• A R1 B
• B R2 C
• ? A R3 C

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adjacent_to

• cell wall adjacent_to cytoplasm
• intron adjacent_to exon
• Golgi apparatus adjacent_to endoplasmic
• reticulum
• periplasm adjacent_to plasma membrane
• presynaptic membrane adjacent_to synaptic cleft

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A adjacent_to B

• every instance of A stands in the instance-level
  adjacent_to relation to some instance of B

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adjacent_to as a relation between universals is
not symmetric

• nucleus adjacent_to cytoplasm
• Not cytoplasm adjacent_to nucleus
• seminal vesicle adjacent_to urinary bladder
• Not urinary bladder adjacent_to seminal vesicle

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The Granularity Gulf

• most existing data-sources are of fixed, single
  granularity
• many (all?) clinical phenomena cross
  granularities

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Main obstacle to integrating genetic and EHR data

No facility for dealing with time and instances
(particulars, individuals) in current ontologies
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Key idea

• To define ontological relations like
• part_of, develops_from
• it is not enough to look just at universals /
  classes / types / concepts
• we need also to take account of instances and time

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transformation_of

• A transformation_of B
• def. any instance of A was at some earlier time
  an instance of B

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transformation_of
mature RNA transformation_of pre-RNA adult
transformation_of child carcinomatous colon
transformation_of colon
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transformation_of relations cross both time and
granularity
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Advantages of the methodology of enforcing
commonly accepted coherent definitions

• promote quality assurance (better coding)
• guarantee automatic reasoning across ontologies
  and across data at different granularities
• yields direct connection to times and instances
  in the EHR