title: GAPSCORE: finding gene and protein names one word at a time.
abstract: BACKGROUND
  New high-throughput technologies have accelerated the accumulation of knowledge about genes and proteins. However, much knowledge is still stored as written natural language text. Therefore, we have developed a new method, GAPSCORE, to identify gene and protein names in text. GAPSCORE scores words based on a statistical model of gene names that quantifies their appearance, morphology and context.
  
  
  RESULTS
  We evaluated GAPSCORE against the Yapex data set and achieved an F-score of 82.5% (83.3% recall, 81.5% precision) for partial matches and 57.6% (58.5% recall, 56.7% precision) for exact matches. Since the method is statistical, users can choose score cutoffs that adjust the performance according to their needs.
  
  
  BACKGROUND
  GAPSCORE is available at http://bionlp.stanford.edu/gapscore/
authors.affiliation: Department of Genetics, Stanford Medical Center, 300 Pasteur Drive, Lane L 301, Mail Code 5120, Stanford, CA 94305-5120, USA.
authors.forename: Jeffrey T
authors.initials: JT
authors.lastname: Chang
authors.forename: Hinrich
authors.initials: H
authors.lastname: Schütze
authors.forename: Russ B
authors.initials: RB
authors.lastname: Altman
mesh_terms: D000043:Abstracting and Indexing; D000465:Algorithms; D001185:Artificial Intelligence; D003628:Database Management Systems; D004014:Dictionaries as Topic; D005796:Genes; D016247:Information Storage and Retrieval / Q000379:methods*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated; D010506:Periodicals as Topic; D011506:Proteins; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software; D009626:Terminology as Topic
chemical_list: D011506:Proteins