Rapid pattern development for concept recognition systems: Application to point mutations

J. Gregory Caporaso, William A. Baumgartner, David A. Randolph, K. Bretonnel Cohen, Lawrence Hunter

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


The primary biomedical literature is being generated at an unprecedented rate, and researchers cannot keep abreast of new developments in their fields. Biomedical natural language processing is being developed to address this issue, but building reliable systems often requires many expert-hours. We present an approach for automatically developing collections of regular expressions to drive high-performance concept recognition systems with minimal human interaction. We applied our approach to develop MutationFinder, a system for automatically extracting mentions of point mutations from the text. MutationFinder achieves performance equivalent to or better than manually developed mutation recognition systems, but the generation of its 759 patterns has required only 5.5 expert-hours. We also discuss the development and evaluation of our recently published high-quality, human-annotated gold standard corpus, which contains 1,515 complete point mutation mentions annotated in 813 abstracts. Both MutationFinder and the complete corpus are publicly available at http://mutationfinder.sourceforge.net/.

Original languageEnglish (US)
Pages (from-to)1233-1259
Number of pages27
JournalJournal of Bioinformatics and Computational Biology
Issue number6
StatePublished - Dec 2007


  • Biomedical natural language processing
  • Concept recognition
  • Corpus construction
  • Information extraction
  • Mutations
  • Pattern learning
  • Text mining

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications


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