Evaluation of techniques for increasing recall in a dictionary approach to gene and protein name identification.
Gene and protein name identification in text requires a dictionary approach to relate synonyms to the same gene or protein, and to link names to external databases. However, existing dictionaries are incomplete. We investigate two complementary methods for automatic generation of a comprehensive dictionary: combination of information from existing gene and protein databases and rule-based generation of spelling variations. Both methods have been reported in literature before, but have hitherto not been combined and evaluated systematically. We combined gene and protein names from several existing databases of four different organisms. The combined dictionaries showed a substantial increase in recall on three different test sets, as compared to any single database. Application of 23 spelling variation rules to the combined dictionaries further increased recall. However, many rules appeared to have no effect and some appear to have a detrimental effect on precision.
Schuemie
Martijn J
MJ
Department of Medical Informatics, Erasmus University Medical Center Rotterdam, P.O. Box 1738, 3000 DR, Rotterdam, The Netherlands. m.schuemie@erasmusmc.nl
Mons
Barend
B
Weeber
Marc
M
Kors
Jan A
JA
D000043:Abstracting and Indexing; D000818:Animals; D001331:Automation; D019295:Computational Biology; D003201:Computers; D030541:Databases, Genetic; D030562:Databases, Protein; D004014:Dictionaries as Topic; D006801:Humans; D016247:Information Storage and Retrieval; D015233:Models, Statistical; D009275:Names; D009323:Natural Language Processing; D009626:Terminology as Topic