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Rapid pattern development for concept recognition systems: application to point mutations.

J Gregory Caporaso1, William A Baumgartner, David A Randolph

  • 1Department of Biochemistry and Molecular Genetics, Center for Computational Pharmacology, University of Colorado Health Sciences Center, Aurora, CO, USA. gregcaporaso@gmail.com

Journal of Bioinformatics and Computational Biology
|January 4, 2008
PubMed
Summary
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Researchers developed an automated method to extract point mutations from biomedical literature, significantly reducing expert time. This approach powers MutationFinder, achieving high performance with minimal manual input for concept recognition.

Area of Science:

  • Biomedical informatics
  • Computational biology
  • Natural Language Processing

Background:

  • The rapid growth of biomedical literature overwhelms researchers' ability to stay current.
  • Developing reliable biomedical Natural Language Processing (NLP) systems demands extensive expert time.

Purpose of the Study:

  • To present an automated approach for generating regular expressions to build high-performance concept recognition systems.
  • To develop MutationFinder, a system for automatically extracting point mutation mentions from text with minimal human interaction.

Main Methods:

  • Developed an automated method for generating regular expression collections.
  • Applied the approach to create MutationFinder for point mutation extraction.
  • Utilized a newly developed, human-annotated gold standard corpus for evaluation.

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Main Results:

  • MutationFinder achieved performance comparable to or exceeding manually developed systems.
  • The system's 759 patterns were generated in only 5.5 expert-hours.
  • A high-quality corpus of 1,515 point mutation mentions was annotated and evaluated.

Conclusions:

  • Automated regular expression generation significantly reduces the expert effort required for biomedical NLP.
  • MutationFinder offers an efficient and effective solution for extracting point mutation information.
  • The publicly available MutationFinder system and corpus facilitate further research in biomedical text mining.