Inter-species normalization of gene mentions with GNAT.
BACKGROUND
Text mining in the biomedical domain aims at helping researchers to access information contained in scientific publications in a faster, easier and more complete way. One step towards this aim is the recognition of named entities and their subsequent normalization to database identifiers. Normalization helps to link objects of potential interest, such as genes, to detailed information not contained in a publication; it is also key for integrating different knowledge sources. From an information retrieval perspective, normalization facilitates indexing and querying. Gene mention normalization (GN) is particularly challenging given the high ambiguity of gene names: they refer to orthologous or entirely different genes, are named after phenotypes and other biomedical terms, or they resemble common English words.


RESULTS
We present the first publicly available system, GNAT, reported to handle inter-species GN. Our method uses extensive background knowledge on genes to resolve ambiguous names to EntrezGene identifiers. It performs comparably to single-species approaches proposed by us and others. On a benchmark set derived from BioCreative 1 and 2 data that contains genes from 13 species, GNAT achieves an F-measure of 81.4% (90.8% precision at 73.8% recall). For the single-species task, we report an F-measure of 85.4% on human genes.


BACKGROUND
A web-frontend is available at http://cbioc.eas.asu.edu/gnat/. GNAT will also be available within the BioCreativeMetaService project, see http://bcms.bioinfo.cnio.es.


BACKGROUND
The test data set, lexica, and links toexternal data are available at http://cbioc.eas.asu.edu/gnat/
Hakenberg
Jörg
J
Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA. joerg.hakenberg@asu.edu
Plake
Conrad
C
Leaman
Robert
R
Schroeder
Michael
M
Gonzalez
Graciela
G
D000465:Algorithms; D001185:Artificial Intelligence; D030541:Databases, Genetic; D005796:Genes / Q000235:genetics*; D016247:Information Storage and Retrieval / Q000379:methods*; D016239:MEDLINE; D009323:Natural Language Processing; D010506:Periodicals as Topic; D012984:Software; D013045:Species Specificity