Suregene, a scalable system for automated term disambiguation of gene and protein names.
Researchers, hindered by a lack of standard gene and protein-naming conventions, endure long, sometimes fruitless, literature searches. A system that is able to automatically assign gene names to their LocusLink ID (LLID) in previously unseen MEDLINE abstracts is described. 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, 7344 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.
Podowski
Raf M
RM
Karolinska Institutet, SE-171 77 Stockholm, Sweden. rpodowski@cmmt.ubc.ca
Cleary
John G
JG
Goncharoff
Nicholas T
NT
Amoutzias
Gregory
G
Hayes
William S
WS
D000465:Algorithms; D030562:Databases, Protein; D005796:Genes; D006801:Humans; D016239:MEDLINE; D009323:Natural Language Processing; D011506:Proteins / Q000145:classification*; D012984:Software; D009626:Terminology as Topic; D018875:Vocabulary, Controlled
D011506:Proteins