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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Using text to build semantic networks for pharmacogenomics.

Adrien Coulet¹, Nigam H Shah, Yael Garten

¹Department of Medicine, 300 Pasteur Drive, Room S101, Mail Code 5110, Stanford University, Stanford, CA 94305, USA.

Journal of Biomedical Informatics

|August 21, 2010

Summary

This study introduces a novel Natural Language Processing (NLP) approach to extract pharmacogenomics (PGx) relationships from scientific literature. The developed method creates a computable network of PGx knowledge for improved drug discovery and treatment.

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Area of Science:

Computational Biology
Bioinformatics
Natural Language Processing

Background:

Pharmacogenomics (PGx) knowledge is largely unstructured within published studies, limiting automated computation.
Existing Natural Language Processing (NLP) methods for relationship extraction often require domain-specific rules or ontologies, which are scarce in emerging fields like PGx.

Purpose of the Study:

To develop an automated method for extracting pharmacogenomics relationships from MEDLINE abstracts.
To construct a computable network of PGx knowledge to aid in drug discovery and clinical application.

Main Methods:

Utilized NLP techniques, specifically syntactic parsing, on a large corpus of 17 million MEDLINE abstracts (over 87 million sentences).
Built a pharmacogenomics ontology from a lexicon of key entities and extracted relationships based on syntactic structures.
Mapped extracted relationships to a common schema, achieving high precision (70-87.7%).

Main Results:

Successfully extracted a network of 40,000 relationships involving over 200 entity types, including genes, drugs, phenotypes, and their modifiers.
Identified relationships between entities such as VKORC1, warfarin, and clotting disorders, as well as their related polymorphisms and treatment responses.
Demonstrated high precision in extracted pharmacogenomic relationships.

Conclusions:

The developed NLP approach enables automated extraction of structured pharmacogenomics knowledge from unstructured text.
The resulting computable network of PGx relationships can guide knowledge curation and facilitate discovery in precision medicine.
This method addresses the lack of available rules and ontologies in the rapidly evolving field of pharmacogenomics.