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BioTagger-GM: a gene/protein name recognition system.

Manabu Torii1, Zhangzhi Hu, Cathy H Wu

  • 1The Imaging Science and Information Systems Center, Department of Oncology, Georgetown University Medical Center, 2115 Wisconsin Avenue NW, Washington, DC 20057, USA. torii@isis.georgetown.edu

Journal of the American Medical Informatics Association : JAMIA
|December 17, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces BioTagger-GM, an advanced system for biomedical named entity recognition (BNER) of gene and protein names. It effectively integrates terminology sources and machine learning for superior knowledge mining from text.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Natural Language Processing

Background:

  • Biomedical Named Entity Recognition (BNER) is crucial for extracting knowledge from biomedical literature.
  • Gene and protein name recognition is a primary focus within BNER.
  • Existing systems often benefit from leveraging diverse information sources and advanced computational methods.

Purpose of the Study:

  • To develop BioTagger-GM, a novel system for gene and protein name recognition.
  • To exploit rich information from terminology sources using machine learning and system combination.
  • To enhance automated biomedical knowledge discovery in free text.

Main Methods:

  • Dictionary lookup using BioThesaurus and UMLS Metathesaurus for gene/protein tagging.
  • Machine learning models trained with dictionary lookup results as features.
  • Post-processing with heuristic rules and system combination via a voting scheme.

Main Results:

  • BioTagger-GM achieved a high F-Measure of 0.8887 on the BioCreAtIvE II GM corpus.
  • Performance surpassed the top system in the BioCreAtIvE II challenge.
  • The system demonstrated general applicability on the JNLPBA corpus.

Conclusions:

  • Integrating terminology sources, machine learning, and system combination is effective for BNER.
  • BioTagger-GM represents a significant advancement in gene/protein name recognition.
  • The approach facilitates more robust automated biomedical knowledge extraction.