Transcript BIRN ONTOLOGIES Session

BIRN ONTOLOGIES Session
Jeffrey Grethe
Amarnath Gupta
Bertram Ludäscher
Maryann E. Martone
All Hands Meeting 2003
Overview
 First half:
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Ontologies (brief; not a “total recall”) (Bertram)
UMLS & “Bonfire” extensions (Jeff)
Disease maps (Maryann, Amarnath)
Ontology-enhanced tools (Maryann)
 Second half:
• Discussion on policy issues, BIRN ontology curation etc
Kinds of “Ontologies” (simplified
cheat sheet)
1.
Controlled Vocabularies
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2.
concept
concept
concept
agreed upon range of values (“enumeration type”)
e.g. “standard names” for materials, diseases, …
Simple Taxonomies, Classification hierarchies (isa)

controlled concept vocabulary
+ subconcept (specialization) relationship
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e.g. biological taxonomies
3.
4.
concept
isa
concept
part-of
concept
Graph-like Ontologies
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isa, has-a, and other relationships
relationship
concept
concept
(contained_in, causes, activates, …),
the latter usually w/o formalized semantics (but agreed upon)
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e.g. semantic nets, RDF, …
Parent  Personoffspring.Person
Full-fledged Ontologies

Usually logic-based ontologies; relationships (including isa) are logic consequences of
formal concept definitions  (partially) defined semantics
class
You knew this already: e.g. 5. Conceptual Models

DSM-IV(Diagnostic and Statistical Manual of Mental Disorders )
290.11 isa 290.1 isa 290
class
relation
class
DSM IV Taxonomy
290 Dementia of the Alzheimer's Type, With Late Onset, Uncomplicated
290.1 Dementia Due to Creutzfeldt-Jakob Disease
290.1 Dementia Due to Pick's Disease
290.1 Dementia of the Alzheimer's Type, With Early Onset, Uncomplicated
290.11 Dementia of the Alzheimer's Type, With Early Onset, With Delirium
290.12 Dementia of the Alzheimer's Type, With Early Onset, With Delusions
290.13 Dementia of the Alzheimer's Type, With Early Onset, With Depressed Mood
290.2 Dementia of the Alzheimer's Type, With Late Onset, With Delusions
290.21 Dementia of the Alzheimer's Type, With Late Onset, With Depressed Mood
290.3 Dementia of the Alzheimer's Type, With Late Onset, With Delirium
290.4 Vascular Dementia, Uncomplicated
290.41 Vascular Dementia, With Delirium
290.42 Vascular Dementia, With Delusions
290.43 Vascular Dementia, With Depressed Mood
291 Alcohol Intoxication Delirium
Uses of Ontologies in Data
Integration
 “Smart” (conceptual-level) data discovery, browsing, querying
• looking for “C”, finding “D” (which is C-related)
• terminological and semantic “glue” between different “data worlds”
 Conceptual / Semantic Modeling of a domain
• for terminologies: “domain maps” (in description logic)
• for processes: “process maps”, “disease maps” (open issue)
 …
 Need to provide …
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Ontology exchange syntax
Ontology extensions mechanisms ( BONFIRE)
Inter-ontology mapping mechanisms (yours vs. mine)
Data-to-ontology registration mechanisms (data to concepts)
Generic Standards
 RDF, RDFS
• For graph-based ontologies (=explicit statements)
relationship
concept
concept
 OWL (Web Ontology Language)
Parent  Personoffspring.Person
• Three levels: OWL Lite, OWL DL, OWL Full
• Some Features:
 Ontology O2 uses (refers to) O1 (over the web!)
 formalism to exchange, extended, and map between ontologies
Generic Tools
 Ontology authoring: Protégé-2000 ontology tool (Stanford)
• OWL Plug-In (evolving)
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Developers’ corner:
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Jena-2 Semantic Web Framework (HP), for dealing with OWL ontologies
Logic programming extensions (SWI)
Example: Ontology-enhanced Map
Integration (OMI)
1. Upload ontologies
O1, O2, … (O2, O3 use O1,…)
2. Upload ontology mapping
Om :: Oa  Ob
3. Register data sets
D1, D2, … to ontology Oa
4. Query data sets through Ob interface!
UMLS &
BONFIRE (Community Ontology Building)
What is UMLS?
 UMLS is a long-term research project began on
1986 by the National Library of Medicine (NLM)
 UMLS is a collection of knowledge sources designed
to facilitate the retrieval and integration of
information from multiple machine-readable
biomedical information sources
Knowledge Resources
 Metathesaurus
• The Metathesaurus is organized by concept or meaning, it
provides a uniform, integrated distribution format from
about 60 biomedical vocabularies and classifications and
links many different names for the same concepts
• The 2000 edition of the Metathesaurus includes more than
730,000 concepts and 1.5 million concept names from
over 50 different biomedical vocabularies, some in multiple
languages
Knowledge Resources
 Semantic Network
• Semantic Network contains information about the types or
categories (e.g., "Disease or Syndrome," "Virus") to which
all concepts have been assigned and the permissible
relationships among these types (e.g., "Virus" causes
"Disease or Syndrome")
• The semantic types are the nodes in the Network, and the
relationships between them are the links.It has 132
semantic types, 53 links between the semantic types.
Knowledge Resources
 Semantic Network
• Semantic types: organisms, anatomical structures,
biologic function, chemicals, events, physical objects,
and concepts or ideas etc.
• Relations: ‘isa’, `physically related to,' `spatially
related to,' `temporally related to,' `functionally related
to,' and `conceptually related to’ etc.
Hierarchical relations types:
Associative (non-isa) Relationships
Knowledge Resources
 Information Sources Map
• The information sources are varied and include
bibliographic databases, diagnostic expert systems, and
factual databases
• The Information Sources Map or directory contains both
human-readable and machine-"processable" information
about the scope, location, vocabulary, syntax rules, and
access conditions of biomedical databases of all kinds
Related Sites
Further Information:
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http://www.nlm.nih.gov/
http://www.nlm.nih.gov/research/umls/
http://www.nlm.nih.gov/research/umls/UMLSDOC.HTML
http://www.nlm.nih.gov/research/umls/umlsmain.html
BONFIRE
 BONFIRE will allow BIRN users to accommodate
concepts not present in the available pre-defined
source ontologies
 Whenever possible, users should employ the
relationship terms provided within the UMLS or other
source ontologies provided by BIRN
 Once new terms are defined, when will they will
become part of the BIRN Ontology (BONFIRE)?
• after appropriate curation?
Ontology Refinement
An Example Data Set
 species = rat (UMLS: C003493)
 region = neostriatum (UMLS: C0162512)
 cell type = medium spiny cell (No Concept Available)
 structure = spiny dendrite(No Concept Available)
 segmented object = dendritic spine (UMLS:
C0872341)
 segmented object = dendritic shaft (No Concept
Available)
BONFIRE Example
 For this data set, no ontology IDs exist for medium spiny cell, spiny dendrite or
dendritic shaft.
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medium spiny cell (BONFIRE: BID006)
medium spiny cell “is a” neuron (UMLS: C0027882)
medium spiny cell “has location” neostriatum (UMLS: C0162512)
medium spiny cell “is a” neuron AND “has property” dendritic spine (UMLS:
C0872341)
 spiny dendrite (BONFIRE: BID007)
 spiny dendrite “is a” dendrite (UMLS: C0011305)
 spiny dendrite ‘contains” dendritic spine (UMLS: C0872341)
DISEASE MAPS
Glue Knowledge for Mouse BIRN
Navigating through Multi-resolution
information
Linking animal and human imaging
data
brain
Entopeduncular
nucleus
Globus pallidus,
internal segment
Animal Model
Disease Process
cerebellum
cerebellar cortex
Purkinje cell
dendritic spine
•Link database concepts to UMLS/Neuronames
•Utilize the neurohomology ontology: M. Bota at
USC
•Develop disease and animal model knowledge
maps
Knowledge Maps
Parkinson’s Disease
C0030567
Pathological feature
symptom
neuronal degeneration
C0027746
akinesia
C027746
tremor
rigidity
Lewy Body
C0085200
Dopamine neuron
Cell inclusion
Motor deficit
C0205708
Filamentous inclusion
C0230674
Substantia nigra
neurons
glia
C0041538
C0175412
C0027882
Abnormal filaments
ubiquitin
C0815003
Alpha synuclein
cortex
C0007776
C0027836
C024566
Basal forebrain
C0746626
Knowledge Map: Animal Model
transgenic animal
Alpha synuclein
a-synuclein mouse
C0025936
C024566
Cellular phenotype
Behavioral phenotype
Cellular inclusion
nuclear inclusion
Cytoplasmic inclusion
C0544907
ubiquitin
Motor deficit
Alpha synuclein
C0041538
neurons
C0027882
glia
C0027836
C0205708
C024566
C0746626
Knowledge Maps
Parkinson’s Disease
a-synuclein mouse
Pathological feature
nuclear
inclusion
Cellular phenotype
Lewy Body
Cytoplasmic
inclusion
Cellular inclusion
Alpha
synuclein
Filamentous inclusion
neurons
ubiquitin
glia
Parkinson’s disease map
disease
C0012634
course of illness
C0242656
disease phase
C0457338
pathology
C0677042
disease characteristic
C0599878
symptoms
C0683368
Pathological process
C0030660
sign/symptom
C0037088
proneness/risk
C0178598
severities
C0439793
epidemiology
C0699910
disease classification
C0683326
prevention, intervention and treatment
C0679677
Parkinson’s disease features
Concept A
Parkinsons disease
pathological feature
Lewy Body
relationship
C0085200
Lewy Body
Lewy Body
filamentous inclusion
C0541641
abnormal filament
abnormal filament
abnormal filament
Lewy Body
Lewy Body
Lewy Body
Lewy Body
Lewy Body
Patients with mutation in parkin
Concept B
has a
is a
is a
pathological feature
histopathological feature C0243140
histopathological feature
is a
is a
has part of
consists of
do not consist of
do not consist of
contains
has property
has component
has componetn
has component
do not have
cell inclusion
filamentous inclusion
abnormal filament
alpha synuclein
beta synuclein
gamma synuclein
ubiquitin
eosinophilia
Parkin
synphilin-1
tau
Lewy Bodies
C0205708
C0541641
C0245466
C0041538
C0014457
Parkinson’s disease processes
Concept A
relationship
Concept B
alpha synuclein protofibrils are toxic to DA neurons; filaments are protective
A53T
A30P
A30P
protofibrils
synaptic vesicles
protofibrils
permeabilization of synaptic vesicles
leaky vesicles
increased DA in cytoplasm
increases
increases
inhibits
permeabilize
have a
permeabilize
results in
in
causes
protofilaments
protofilaments
filament formation from protofilaments
membranes
membrane
synaptic vesicles
leaky vesicles
DA neurons
cell death
Object-Oriented Modeling
TOOLS
 Custom: Know-ME, OMI, …
 Generic: Protégé-2000, …
Discussion
Getting Organized
 …
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