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Relations in Biomedical Ontologies

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Title: Relations in Biomedical Ontologies


1
Relations in Biomedical Ontologies
  • Barry Smith
  • Department of Philosophy, University at Buffalo
  • National Center for Biomedical Ontology
    (http//ncbo.us)

2
Two strategies for creating terminologies and
database schemas
  • Ad hoc creation by each clinical or research
    communityvs.
  • Pre-established reference ontologies upon which
    specific local applications can draw

3
We know that high-quality ontologies can help
  • in creating better mappings between human and
    model organism phenotypes
  • S Zhang, O Bodenreider, Alignment of Multiple
    Ontologies of Anatomy Deriving Indirect Mappings
    from Direct Mappings to a Reference Ontology,
    AMIA 2005

4
Advantages of the methodology of shared
coherently defined definitions
  • promotes quality assurance (better coding)
  • guarantees automatic reasoning across ontologies
    and across data at different granularities
  • yields direct connection to temporally indexed
    instance data

5
A basic distinction
  • type vs. instance
  • science text vs. clinical document
  • man vs. Michael

6
Instances are not represented in an ontology
  • For ontology, it is the scientific
    generalizations that are important
  • (but instances must still be taken into account)

7
A 515287 DC3300 Dust Collector Fan
B 521683 Gilmer Belt
C 521682 Motor Drive Belt

8
Ontology Types Instances




9
Ontology A Representation of Types




10
Ontology A Representation of Types
  • Each node of an ontology consists of
  • preferred term (aka term)
  • term identifier (TUI, aka CUI)
  • synonyms
  • definition, glosses, comments

11
Ontology A Representation of Types
Nodes in an ontology are connected by
relations primarily is_a ( is subtype of) and
part_of designed to support search, reasoning and
annotation
12
Motivation To capture reality
  • Inferences and decisions we make are based upon
    what we know of reality.
  • An ontology is a computable representation of
    biological reality, which is designed to enable a
    computer to reason over the data we collect about
    this reality in (some of) the ways that we do.

13
OBO Relation Ontology
14
First step
  • Alignment of OBO Foundry ontologies through a
    common system of formally defined relations in
    the OBO Relation Ontology
  • See Relations in Biomedical Ontologies, Genome
    Biology Apr. 2005

15
  • Judith Blake
  • The use of bio-ontologies ensures consistency
    of data curation, supports extensive data
    integration, and enables robust exchange of
    information between heterogeneous informatics
    systems. ..
  • ontologies formally define relationships
    between the concepts.

16
"Gene Ontology Tool for the Unification of
Biology"
  • an ontology "comprises a set of well-defined
    terms with well-defined relationships"
  • (Ashburner et al., 2000, p. 27)

17
is_a (sensu UMLS)
  • A is_a B def
  • A is narrower in meaning than B
  • grows out of the heritage of dictionaries
  • (which ignore the basic distinction between types
    and instances)

18
is_a
  • congenital absent nipple is_a nipple
  • cancer documentation is_a cancer
  • disease prevention is_a disease
  • Nazism is_a social science

19
is_a (sensu logic)
  • A is_a B def
  • For all x, if x instance_of A then x instance_of
    B
  • cell division is_a biological process
  • adult is_a child ???

20
Two kinds of entities
  • occurrents (processes, events, happenings)
  • cell division, ovulation, death
  • continuants (objects, qualities, ...)
  • cell, ovum, organism, temperature of organism,
    ...

21
is_a (for occurrents)
  • A is_a B def
  • For all x, if x instance_of A then x instance_of
    B
  • cell division is_a biological process

22
is_a (for continuants)
  • A is_a B def
  • For all x, t if x instance_of A at t then x
    instance_of B at t
  • abnormal cell is_a cell
  • adult human is_a human
  • but not adult is_a child

23
Part_of as a relation between types is more
problematic than is standardly supposed
  • heart part_of human being ?
  • human heart part_of human being ?
  • human being has_part human testis ?
  • human testis part_of human being ?

24
two kinds of parthood
  • between instances
  • Marys heart part_of Mary
  • this nucleus part_of this cell
  • between types
  • human heart part_of human
  • cell nucleus part_of cell

25
Definition of part_of as a relation between types
  • A part_of B Def all instances of A are
    instance-level parts of some instance of B
  • ALLSOME STRUCTURE

26
part_of (for occurrents)
  • A part_of B Def
  • For all x, if x instance_of A then there is some
    y, y instance_of B and x part_of y
  • where part_of is the instance-level part
    relation

27
part_of (for continuants)
  • A part_of B def.
  • For all x, t if x instance_of A at t then there
    is some y, y instance_of B at t and x part_of y
  • where part_of is the instance-level part
    relation
  • ALL-SOME STRUCTURE

28
How to use the OBO Relation Ontology
  • Ontologies are representations of types and of
    the relations between types
  • The definitions of these relations involve
    reference to times and instances, but these
    references are washed out when we get to the
    assertions (edges) in the ontology
  • But curators should still be aware of the
    underlying definitions when formulating such
    assertions

29
part_of (for occurrents)
  • A part_of B Def
  • For all x, if x instance_of A then there is some
    y, y instance_of B and x part_of y
  • where part_of is the instance-level part
    relation

30
A part_of B, B part_of C ...
  • The all-some structure of such definitions allows
  • cascading of inferences (true path rule)
  • (i) within ontologies
  • (ii) between ontologies
  • (iii) between ontologies and repositories of
    instance-data

31
Strengthened true path rule
  • Whichever A you choose, the instance of B of
    which it is a part will be included in some C,
    which will include as part also the A with which
    you began
  • The same principle applies to the other relations
    in the OBO-RO
  • located_at, transformation_of, derived_from,
    adjacent_to, etc.

32
Kinds of relations
  • Between types
  • is_a, part_of, ...
  • Between an instance and a type
  • this explosion instance_of the type explosion
  • Between instances
  • Marys heart part_of Mary

33
In every ontology
  • some terms and some relations are primitive
    they cannot be defined (on pain of infinite
    regress)
  • Examples of primitive relations
  • identity
  • instantiation
  • (instance-level) part_of
  • (instance-level) continuous_with

34
transformation_of
35
transformation_of
  • A transformation_of B Def.
  • Every instance of A was at some earlier time an
    instance of B
  • adult transformation_of child

36
tumor development
37
derives_from
C1 c1 at t1
C c at t
time
C' c' at t
ovum
zygote derives_from
sperm
38
two continuants fuse to form a new continuant
C1 c1 at t1
C c at t
C' c' at t
fusion
39
one initial continuant is replaced by two
successor continuants
C1 c1 at t1
C c at t
C2 c2 at t1
fission
40
one continuant detaches itself from an initial
continuant, which itself continues to exist
C c at t
c at t1
C1 c1 at t
budding
41
one continuant absorbs a second continuant while
itself continuing to exist
c at t1
C c at t
C' c' at t
capture
42
A suite of defined relations between types
Foundational is_apart_of
Spatial located_incontained_inadjacent_to
Temporal transformation_ofderives_frompreceded_by
Participation has_participanthas_agent
43
To be added to the Relation Ontology
  • lacks (between an instance and a type, e.g. this
    fly lacks wings)
  • dependent_on (between a dependent entity and its
    carrier or bearer)
  • quality_of (between a dependent and an
    independent continuant)
  • functioning_of (between a process and an
    independent continuant)

44
tumor development
C1
C c at t
c at t1
45
The Granularity Gulf
  • most existing data-sources are of fixed, single
    granularity
  • many (all?) clinical phenomena cross
    granularities

46
transformation_of
47
Not only relations
  • we applied the same methodology to other
    top-level categories in ontology, e.g.
  • process
  • function
  • boundary
  • act, observation
  • tissue, membrane, sequence

48
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 EHR

49
TLR2MyD88 complex
has_output
TLR2-MyD88 binding
has_disposition
has_participant
TLR2
LTA binding
has_participant
MyD88
regulated_by
preceded_by
has_lower_level_granularity
has_part
process
TLR2-TLR2 ligand binding
has_participant
TIR domain
TIR-TIR binding
TLR-2 signalling pathway
50
The methodology of cross-products
  • compound terms in Foundry ontologies should be
    post-composed out of simpler terms linked via
    relational expressions from the RO Relation
    Ontology
  • Eg composing PATO increased concentration with
    FMA blood and CheBI glucose to represent
    increased blood glucose phenotypes.

51
The methodology of cross-products
  • enforcing use of RO in linking terms drawn from
    Foundry ontologies serves
  • to reduce arbitrariness and ambiguity which
    marks existing approaches to post-composition
  • makes the results of post-composition
    algorithmically processable in virtue of the
    logical definitions provided by the RO

52
TLR2MyD88 complex
has_output
TLR2-MyD88 binding
has_disposition
has_participant
TLR2
LTA binding
has_participant
MyD88
regulated_by
preceded_by
has_lower_level_granularity
has_part
process
TLR2-TLR2 ligand binding
has_participant
TIR domain
TIR-TIR binding
TLR-2 signalling pathway
53
The Granularity Gulf
  • most existing data-sources are of fixed, single
    granularity
  • many (all?) clinical phenomena cross
    granularities
  • Therefore need to reason across time, tracking
    the order of events in time

54
GOs three ontologies
biological process
molecular function
dependent
cellular component
independent
55
GOs three ontologies
organism-level biological process
cellular process
molecular function
cellular component
56
Normalization of Granular Levels
molecular function
cellular process
organism-level biological process
molecule
cellular component
organism
57
molecule
cellular component
organism
58
molecule
cellular component
organism
59
molecular location
cellular location
organism-level location
60
The GO is a canonical representation
  • The Gene Ontology is a computational
    representation of the ways in which gene products
    normally function in the biological realm
  • Nucl. Acids Res. 2006 34.

61
everything here is typical
62
The Methodology of Annotations
  • Scientific curators use experimental observations
    reported in the biomedical literature to link
    gene products with GO terms in annotations.
  • The gene annotations taken together yield a
    slowly growing computer-interpretable map of
    biological reality.
  • The process of annotating literature also leads
    to improvements and extensions of the ontology,
    which institutes a virtuous cycle of improvement
    in the quality and reach of both future
    annotations and the ontology itself.

63
When we annotate the record of an experiment
  • we use terms representing types to capture what
    we learn about
  • this experiment (instance), performed here and
    now, in this laboratory
  • the instances experimented upon
  • These instances are typical they are
    representatives of types
  • of experiment (described in FuGO)
  • of gene product molecules, molecular functions,
    cellular components, biological processes
    (described in GO)

64
Experimental records
  • document a variety of instances (particular
    real-world examples or cases), ranging from
    instances of gene products (including individual
    molecules) to instances of biochemical processes,
    molecular functions, and cellular locations

65
Experimental records
  • provide evidence that gene products of given
    types have molecular functions of given types by
    documenting occurrences in the real world that
    involve corresponding instances of functioning.
  • They document the existence of real-world
    molecules that have the potential to execute
    (carry out, realize, perform) the types of
    molecular functions that are involved in these
    occurrences.

66
  • Glossary
  • Instance A particular entity in spatio-temporal
    reality.
  • Type A general kind instantiated by an
    open-ended totality of instances which share
    certain qualities and propensities in common of
    the sort that can be documented in scientific
    literature

67
Glossary
  • Gene product instance A molecule that is
    generated by the expression of a DNA sequence and
    which plays some significant role in the biology
    of the organism.
  • Gene product type A type of gene product
    instance.

68
Glossary
  • Biological process instance (aka occurrence) A
    change or complex of changes on the level of
    granularity of the cell or organism, mediated by
    one or more gene products.
  • Biological process type A type of biological
    process instance.

69
Glossary
  • Cellular component instance A part of a cell,
    including cellular structures, macromolecular
    complexes and spatial locations identified in
    relation to the cell
  • Cellular component type A type of cellular
    component.

70
Glossary
  • Molecular function instance The propensity of a
    gene product instance to perform actions, such as
    catalysis or binding, on the molecular level of
    granularity.
  • Molecular function type A type of molecular
    function instance.

71
Glossary
  • Molecular function execution instance (aka
    functioning) A process instance on the
    molecular level of granularity that is the result
    of the action of a gene product instance.
  • Molecular function execution type A type of
    molecular function execution instance (aka a
    type of functioning)

72
molecular location
cellular locations
organism-level locations
73
organism-level physiology
molecular process
cellular physiology
molecular function (GO)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy
anatomy (fly, fish, human...)
74
organism-level physiology
cellular physiology
molecular process
normal (functionings)
molecular function (GO)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy
anatomy (fly, fish, human...)
75
pathophysiology (disease)
pathological (malfunctionings)
pathoanatomy (fly, fish, human ...)
76
pathophysiology (disease)
organism-level physiology
cellular physiology
molecular process
molecular function (GO)
pathoanatomy (fly, fish, human ...)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy (GO)
anatomy (fly, fish, human...)
77
pathophysiology (disease)
organism-level physiology
cellular physiology
molecular process
molecular function (GO)
phenotype
pathoanatomy (fly, fish, human ...)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy
anatomy (fly, fish, human...)
78
pathophysiology (disease)
organism-level physiology
cellular physiology
molecular process
molecular function (GO)
phenotype
pathoanatomy (fly, fish, human ...)
cell (types)
species
ChEBI, Sequence, RNA ...
cellular anatomy
anatomy (fly, fish, human...)
investigation (FuGO)
79
End
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