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)

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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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Aim

● Automatic relationship extraction.● Entity mapping in a schema.

– Semantic network structure.

● Curation of PGx knowledge.● Resource for knowledge discovery.

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However...

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What is the meaning of Pharmacogenomics?

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Pharmacogenomics (1)

Pharmaco Genomics PGx

Φάρμακο Γίνομαι

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Pharmacogenomics (2)

● How genetic variation influences drug response in patients.

● Most of this knowledge presented in binary relationships.

R(a,b)

Relationship ObjectSubject

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Is This Something New?

● Co-occurrence approach:– Pharmexpresso.

– Tri-co-occurrences.

● Syntactic parser approach:– OpenDMAP.

– Vocabularies.

Complex relationship semantics.

Manual relationship evaluation.

Explicit relationship identification.

Large pattern sets.

Stable ontologies.

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So...

Regular gene expression networksDrug-disease networks

Molecular interaction networksGene-disease networks

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Method Overview

MEDLINEAbstracts

DependencyGraphs ofSentences

R

Ontology

PGx network

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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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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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Parsing Example“Several single nucleotide polymorphisms (SNPs) in VKORC1 are associated

with warfarin dose across the normal dose range”

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Dependency Graph

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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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Dependencies for Seed Expansion

● Expand the seed● End the expansion● Interrupt the expansion

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2b. Relation Extraction

● Seed coupling– Two seeds wend with a normalised verb.

– Relationship creation.

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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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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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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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Example

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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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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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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.

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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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Results (2)

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Results (3)

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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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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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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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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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Questions?

Ερωτήσεις;

Questions? (in French ^_^)

Preguntas?

質問 ?