title: AZuRE, a scalable system for automated term disambiguation of gene and protein names.
abstract: Researchers, hindered by a lack of standard gene and protein-naming conventions, endure long, sometimes fruitless, literature searches. A system is described which is able to automatically assign gene names to their LocusLink ID (LLID) in previously unseen MEDLINE abstracts. The system is based on supervised learning and builds a model for each LLID. The training sets for all LLIDs are extracted automatically from MEDLINE references in the LocusLink and SwissProt databases. A validation was done of the performance for all 20,546 human genes with LLIDs. Of these, 7,344 produced good quality models (F-measure > 0.7, nearly 60% of which were > 0.9) and 13,202 did not, mainly due to insufficient numbers of known document references. A hand validation of MEDLINE documents for a set of 66 genes agreed well with the system's internal accuracy assessment. It is concluded that it is possible to achieve high quality gene disambiguation using scaleable automated techniques.
authors.affiliation: AstraZeneca R&D Boston and Karolinska Institutet. rpodowski@cmmt.ubc.ca
authors.forename: Raf M
authors.initials: RM
authors.lastname: Podowski
authors.forename: John G
authors.initials: JG
authors.lastname: Cleary
authors.forename: Nicholas T
authors.initials: NT
authors.lastname: Goncharoff
authors.forename: Gregory
authors.initials: G
authors.lastname: Amoutzias
authors.forename: William S
authors.initials: WS
authors.lastname: Hayes
mesh_terms: D030562:Databases, Protein; D005796:Genes; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D011506:Proteins / Q000145:classification*; D012984:Software; D009626:Terminology as Topic; D014584:User-Computer Interface; D018875:Vocabulary, Controlled
chemical_list: D011506:Proteins