title: Learning string similarity measures for gene/protein name dictionary look-up using logistic regression.
abstract: BACKGROUND
  One of the bottlenecks of biomedical data integration is variation of terms. Exact string matching often fails to associate a name with its biological concept, i.e. ID or accession number in the database, due to seemingly small differences of names. Soft string matching potentially enables us to find the relevant ID by considering the similarity between the names. However, the accuracy of soft matching highly depends on the similarity measure employed.
  
  
  RESULTS
  We used logistic regression for learning a string similarity measure from a dictionary. Experiments using several large-scale gene/protein name dictionaries showed that the logistic regression-based similarity measure outperforms existing similarity measures in dictionary look-up tasks.
  
  
  BACKGROUND
  A dictionary look-up system using the similarity measures described in this article is available at http://text0.mib.man.ac.uk/software/mldic/.
authors: School of Computer Science, The University of Manchester, Manchester, UK. yoshimasa.tsuruoka@manchester.ac.uk
authors: Yoshimasa
authors: Y
authors: Tsuruoka
authors: John
authors: J
authors: McNaught
authors: Jun'ichi
authors: J
authors: Tsujii
authors: Sophia
authors: S
authors: Ananiadou
mesh_terms: D001185:Artificial Intelligence; D030562:Databases, Protein; D005796:Genes; D016247:Information Storage and Retrieval / Q000379:methods*; D016015:Logistic Models; D009323:Natural Language Processing; D011506:Proteins / Q000145:classification*; D012044:Regression Analysis; D009626:Terminology as Topic
chemical_list: D011506:Proteins