1. Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation
Bioinformatics, July 2003
P.W.Load, R.D.Stevens,A.Brass and C.A.Goble
University of Manchester
Presented by 임 동혁
July 22, 2005

2. Contents Introduction Semantic Similarity Measures
Validating Semantic Similarity
Investigating Semantic and Sequence Similarity
Semantic Searching of GO Annotated Resources
Discussion

3. Introduction Bioinformatics resources Ontologies
In form of sequence, which are then annotated
In scientific natural language as text
Human readable and understandable
Not easy to interpret computationally
Ontologies
Provide a mechanism for capturing a view of a domain in a shareable form
Both accessible by humans and computationally amenable
Provide a set of vocabulary terms that label concepts in the domain
“is-a” relationship between parent and child
“part-of” relationship between part and whole

4. Gene Ontology(1/2) GO comprises three orthogonal taxonomies of aspects
Molecular function
Biological process
Cellular component
GO is a rapidly growing collection of about phrases, representing terms or concepts
Directed Acyclic Graph(DAG)

5. Gene Ontology(2/2) Allow improved querying of databases
Different resources queried with the same term
Shared understanding improve retrieval consistency across resources and recall and precision
One obvious alternative way
Ask for proteins semantically similar to a query protein
Semantic similarity
Taxonomy of biomedical terms
Ex) Medical Subject Heading(MeSH) : similar content(by words)

6. the Gene Ontology
Receptor-associated protein
GO: p=
Transmembrane receptor
GO: p=0.0997
isa
isa
signal transducer
GO: p=0.208
isa
receptor
GO: p=0.124
isa
isa
photoreceptor
GO: p=
molecular function
GO: p=1
isa
Receptor signaling protein
GO: p=0.0281
isa
isa
chaperone
GO: p=0.0102
ligrand
GO: p=0.0460
Two proteins are both annotated as “transmembrane receptor”
(GO: )
Similar semantic description
One as just “receptor”(GO: )
Semantically less similar

7. Semantic Similarity Measure(1/3)
Early techniques (Rada et al, 1989)
Path distances between terms
Assumes that all of semantic links are of equal weight
Poor assumption
Ex) “photoreceptor” and “transmembrane receptor” are semantically more closely related than “chaperone” and “signal transducer”

8. Semantic Similarity Measure(2/3)
Edge could be weighted
The greater distance from root of the graph, the more specific the terms
However, GO varies widely in the distance of nodes from the root
Ex) (GO: ) is 14 terms deep, (GO: ) is only 3 terms deep
Not significantly less semantically precise

9. Semantic Similarity Measure(2/3)
Usage of terms within the corpus (Resnik, 1999)
Use the notion of “information content”
Familiar from most internet search engines
Ex) “chaperone” is a more informative term than “signal transducer”
The former is used several times, the later thousand times
GO: occurs, GO: and GO: have also occurred (“is-a” link are considered)
More informative

10. Probabilities in the Gene Ontology
Each node is annotated with its GO accession and the probability of this term occurring in the SWISS-PROT-Human database
1. Count the number of times each concept occurrs,
2. A concept occurs if a term, or any node its children occur
3. The probability, p(c), for each node is this value, divided by the number of times
(the probability of root node will be 1)

11. Semantic Similarity between terms
Use simplest of measure (Resnik, 1999)
Based on the information content of shared parents of the two terms
S(c1, c2) is the set of parental concepts shared by both c1 and c2
Minimum p(c) : GO allows multiple path
Pms(probability of the minimum subsumer)
Similirity score between two terms
As probability increase, informativeness decrease

12. Validating Semantic Similarity
How do we validate such a measure?
Protein’s sequence relates to its function
Highly similar sequences should be highly semantically similar
Protein sequences in pairs and plotting sequence similarity against semantic ssimilarity should a relationship

13. Adapting the Similarity Measures to GO and SWISS-PROT
“part-of” relationship
Orphan term
Linked them directly to the root
Ex) GO:
Is-a’s links alone
Proteins may be annotated with more than a single term
Wordnet : Maximum similarity
GO : average similarity

14. Comparing Semantic Similarity Across GO Aspects
There is a good correlation between sequence similarity and semantic similarity
The correlation is greater when measured against the “molecular function”

15. The Relationship Between Semantic Similarity and Evidence Codes
TAS : regarded as the highest standard of evidence
When only TAS GO annotation are considered, the correlation is much greater

16. Effect of Using Semantic Links in Semantic Similarity
Consider only links of a single type “is-a” or “part-of”
Little difference between all link and “is-a” : almost link are of “is-a” type
(6167 / 6202)
No links drop in the middle part : proteins share similar (links are included in semantic similarity measure)

17. Analysis(1/2)
Very high semantic similarity but little sequence similarity
“Polymorphic” groups
Two or more classes of protein involved in the same process
Heterodimerize or sub-families
Hyper variable protein families
arbitrary
Mis-annotations
SWISS-PROT “x-like” but in GO “x”
Spelling mistake

18. Analysis(2/2) - Example

19. Semantic Searching of GO Annotated Resources
Develop a search tool
Given query protein against all the others in SWISS-PROT-Human
Generates a ranked list of semantically similar proteins
Ex) “OPSR_HUMAN”

20. Discussion Investigated semantic similarity measure Future work
All cases semantic similarity is correlated with sequence similarity
GO aspect : molecular funstion
Evidence code : “Traceable Author Statement”
Future work
Effect of the different semantic links in ontologies
Co-expression as revealed by microarray experiments
Expect that biological process aspect would be of great use