Inter-species normalization of gene mentions with GNAT
1Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, USA, 2Biotechnological Centre, Technische Universität Dresden, Tatzberg 47–51, 01307 Dresden, 3Transinsight GmbH, Tatzberg 47–51, 01307 Dresden, Germany and 4Department of Biomedical Informatics, Arizona State University, Phoenix, AZ 85004, USA
*To whom correspondence should be addressed.
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Abstract |
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Motivation: 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.
Availability: A web-frontend is available at http://cbioc.eas.asu.edu/gnat/. GNAT will also be available within the BioCreative MetaService project, see http://bcms.bioinfo.cnio.es.
Contact: joerg.hakenberg{at}asu.edu
Supplementary information: The test data set, lexica, and links to external data are available at http://cbioc.eas.asu.edu/gnat/
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