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PGxCorpus, a manually annotated corpus for pharmacogenomics.

Joël Legrand1, Romain Gogdemir2, Cédric Bousquet3

  • 1Université de Lorraine, CNRS, Inria, LORIA, Nancy, France. joel.legrand@inria.fr.

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Summary
This summary is machine-generated.

This study introduces PGxCorpus, a new dataset for pharmacogenomics (PGx) knowledge extraction. It enables automated discovery of gene-drug-phenotype relationships to advance precision medicine.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Pharmacogenomics

Background:

  • Pharmacogenomics (PGx) knowledge is crucial for precision medicine but is largely locked in unstructured text.
  • Existing natural language processing (NLP) tools lack high-quality annotated corpora for the PGx domain, limiting supervised machine learning applications.
  • Automated extraction of PGx information is needed to make scientific knowledge more accessible and reusable.

Purpose of the Study:

  • To introduce PGxCorpus, a manually annotated corpus for the pharmacogenomics domain.
  • To facilitate the automatic extraction of PGx relationships (gene variations, genes, drugs, phenotypes) from scientific literature.
  • To enable the synthesis and summarization of PGx knowledge for improved usability.

Main Methods:

  • Creation of PGxCorpus by manually annotating 945 sentences from 911 PubMed abstracts.
  • Annotation includes key PGx entities such as gene variations, genes, drugs, and phenotypes.
  • Annotation also captures relationships between these identified entities.
  • A baseline experiment was conducted to demonstrate the corpus's utility.

Main Results:

  • PGxCorpus provides a high-quality, annotated resource specifically for the PGx domain.
  • The corpus enables supervised machine learning approaches for PGx knowledge extraction.
  • A baseline experiment demonstrated the corpus's effectiveness in synthesizing and summarizing PGx knowledge.

Conclusions:

  • PGxCorpus addresses the critical need for annotated data in pharmacogenomics research.
  • This resource is expected to significantly advance automated PGx knowledge discovery and its application in precision medicine.
  • The corpus facilitates the development of NLP tools for extracting and utilizing complex PGx relationships from biomedical literature.