1. Using Text to Build Semantics
Networks for Pharmacogenomics
George Karystianis
Adrien Coulet, Nigam Shah, Yael Garten, Mark Musen, Russ B. Altman
Journal of Biomedical informatics (2010)

2. Motivation
● Manually crafted rules to define relationships
between entities.
– Limited scope domains.
● Pharmacogenomics.
– Semantic complexity.
● Enhance the PharmaGKB.
● Large size of literature.
● NLP techniques promising.
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3. Aim
● Automatic relationship extraction.
● Entity mapping in a schema.
– Semantic network structure.
● Curation of PGx knowledge.
● Resource for knowledge discovery.
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4. However...
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5. What is the meaning of
Pharmacogenomics?
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6. Pharmacogenomics (1)
Pharmaco Genomics PGx
Φάρμακο Γίνομαι
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7. Pharmacogenomics (2)
● How genetic variation influences drug
response in patients.
● Most of this knowledge presented in binary
relationships.
R(a,b)
Relationship Subject Object
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8. Is This Something New?
● Co-occurrence approach: Complex relationship
– Pharmexpresso. semantics.
– Tri-co-occurrences. Manual relationship
evaluation.
● Syntactic parser approach: Explicit relationship
identification.
– OpenDMAP.
Large pattern sets.
– Vocabularies.
Stable ontologies.
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9. So...
Gene-disease networks Molecular interaction networks
Drug-disease networks Regular gene expression networks
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10. Method Overview
Ontology
MEDLINE
Abstracts
Dependency
Graphs of
Sentences
R
PGx network
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11. 1a. Sentence Parsing
● Implementation of lexicons for sentence
retrieval.
● Stanford Parser.
● Focused on sentences with at least 2 key PGX
entities.
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12. 1b. Sentence Parsing
● Querying the sentence index using seeds.
– particular terms corresponding to recognized entities.
– focus on gene-drug/gene-phenotype pairs.
● Reducing set/size of parse trees.
● Parse trees -> dependency graphs.
– rooted, oriented, labelled, easy to read, process,
understand than parse trees.
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13. Parsing Example
“Several single nucleotide polymorphisms (SNPs) in VKORC1 are associated
with warfarin dose across the normal dose range”
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14. Dependency Graph
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15. 2a. Relation Extraction
● Sentence analysis for raw relationship
extraction.
● Seed recognition:
– through PharmGKB lexicons.
● Seed expansion:
– edge traversal of DG to see if the seed is a key entity
or a modified entity.
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16. Dependencies for Seed
Expansion
● Expand the seed
● End the expansion
● Interrupt the expansion
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17. 2b. Relation Extraction
● Seed coupling
– Two seeds wend with a normalised verb.
– Relationship creation.
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18. 2c. Relation Extraction
● Evaluation of precision:
– manual precision evaluation of extracting raw
relationships.
– random selection of 220 raw relationships.
– classification-complete and true, incomplete and true,
false.
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19. 3. Ontology Construction
● Identification of R types.
● Hierarchical organisation of R types and E.
– 4 lists: most frequent, the most frequent modified
entities by genes, drugs, phenotype.
● Refine choice available.
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20. 4a. Relationship Normalization
● Application of ontology to relationship
instances.
● Creation of set of normalised relationships.
● Normalization of entity names:
– modified entity name returned in normalized form
according to ontology.
– Decomposition of modified entity to iterate for the
construction of normalised form.
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21. Example
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22. Example
● Seed: VKORC1_polymorphisms.
● Seed concept: Gene.
● Next word: polymorphism.
– refers to a concept modified by Gene.
– synonym of the concept “variant”.
● Normalised word:
– VKORC1_variant.
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23. 4b. Relation Normalization
● Normalization of relationship types.
– search for a role label which matches the relationship.
– the identifier of the corresponding role is the
normalized type.
– creation of knowledge base of PGX relationships.
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24. Did it work?
● Input:
– 17.396.436 MEDLINE abstracts
● Sentences:
– 87.806.828.
● Sentences with pairs of PGx entities:
– 295.569.
● After pruning:
– 41.134 raw relationships, 21.050 gene-drug pair,
20.084 gene-phenotype pair. 24

25. 25

26. Results
● The 200 most frequent raw relationship types:
– 80% of the extracted relationships.
● Creation of an ontology:
– 200 most frequent relationship types and modified
entities called PHARE-PHArmacogenomics
RElationships.
– 237 concepts and 76 roles.
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27. Results (2)
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28. Results (3)
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29. 29

30. 30

31. Discussion (1)
● Identification of both PGx entities.
● Identification of PGx modified entities.
● Use of key entity lexicons for discovery and
normalization of modified entities.
● Record and recognition of modified entities
under very general textual conditions.
● Flexible, precise method.
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32. Discussion (2)
● Concern: lower recall due to the large corpus
size.
– improve precision with full text parsing.
● Applicable to other domains.
– Human effort required for the ontology creation.
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33. Conclusions (1)
● New method for PGX relationship extraction.
● Use of key PGX entities to identify modified
entities.
● Capture and normalization of raw
relationships.
● Automatic labelling of parsed sentences.
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34. Conclusions (2)
● Creation of a knowledge base.
● Creation of relationship summaries between:
– Genes, drugs, phenotypes.
● Novel approach for PGX text processing.
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35. Questions?
(in French ^_^)
Questions?
質問 ?
Ερωτήσεις;
Preguntas?
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