title: Concept-based annotation of enzyme classes.
abstract: BACKGROUND
  Given the explosive growth of biomedical data as well as the literature describing results and findings, it is getting increasingly difficult to keep up to date with new information. Keeping databases synchronized with current knowledge is a time-consuming and expensive task-one which can be alleviated by automatically gathering findings from the literature using linguistic approaches. We describe a method to automatically annotate enzyme classes with disease-related information extracted from the biomedical literature for inclusion in such a database.
  
  
  RESULTS
  Enzyme names for the 3901 enzyme classes in the BRENDA database, a repository for quantitative and qualitative enzyme information, were identified in more than 100,000 abstracts retrieved from the PubMed literature database. Phrases in the abstracts were assigned to concepts from the Unified Medical Language System (UMLS) utilizing the MetaMap program, allowing for the identification of disease-related concepts by their semantic fields in the UMLS ontology. Assignments between enzyme classes and diseases were created based on their co-occurrence within a single sentence. False positives could be removed by a variety of filters including minimum number of co-occurrences, removal of sentences containing a negation and the classification of sentences based on their semantic fields by a Support Vector Machine. Verification of the assignments with a manually annotated set of 1500 sentences yielded favorable results of 92% precision at 50% recall, sufficient for inclusion in a high-quality database.
  
  
  BACKGROUND
  Source code is available from the author upon request.
  
  
  BACKGROUND
  ftp.uni-koeln.de/institute/biochemie/pub/brenda/info/diseaseSupp.pdf.
authors.affiliation: Department of Biochemistry, University of Cologne, Germany. o.hofmann@smail.uni-koeln.de
authors.forename: Oliver
authors.initials: O
authors.lastname: Hofmann
authors.forename: Dietmar
authors.initials: D
authors.lastname: Schomburg
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003628:Database Management Systems; D030562:Databases, Protein; D004282:Documentation / Q000379:methods*; D004798:Enzymes / Q000145:classification* / Q000378:metabolism*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods; D015201:Meta-Analysis as Topic; D008659:Metabolic Diseases / Q000145:classification / Q000201:enzymology*; D009323:Natural Language Processing; D010506:Periodicals as Topic; D039781:PubMed; D018875:Vocabulary, Controlled
chemical_list: D004798:Enzymes