Joachim Wermter1, Katrin Tomanek, Udo Hahn. 1. Jena University Language and Information Engineering Lab, Friedrich-Schiller-Universität Jena, Fürstengraben 30, 07743 Jena, Germany. joachim.wermter@uni-jena.de
Abstract
MOTIVATION: The recognition and normalization of textual mentions of gene and protein names is both particularly important and challenging. Its importance lies in the fact that they constitute the crucial conceptual entities in biomedicine. Their recognition and normalization remains a challenging task because of widespread gene name ambiguities within species, across species, with common English words and with medical sublanguage terms. RESULTS: We present GeNo, a highly competitive system for gene name normalization, which obtains an F-measure performance of 86.4% (precision: 87.8%, recall: 85.0%) on the BioCreAtIvE-II test set, thus being on a par with the best system on that task. Our system tackles the complex gene normalization problem by employing a carefully crafted suite of symbolic and statistical methods, and by fully relying on publicly available software and data resources, including extensive background knowledge based on semantic profiling. A major goal of our work is to present GeNo's architecture in a lucid and perspicuous way to pave the way to full reproducibility of our results. AVAILABILITY: GeNo, including its underlying resources, will be available from www.julielab.de. It is also currently deployed in the Semedico search engine at www.semedico.org.
MOTIVATION: The recognition and normalization of textual mentions of gene and protein names is both particularly important and challenging. Its importance lies in the fact that they constitute the crucial conceptual entities in biomedicine. Their recognition and normalization remains a challenging task because of widespread gene name ambiguities within species, across species, with common English words and with medical sublanguage terms. RESULTS: We present GeNo, a highly competitive system for gene name normalization, which obtains an F-measure performance of 86.4% (precision: 87.8%, recall: 85.0%) on the BioCreAtIvE-II test set, thus being on a par with the best system on that task. Our system tackles the complex gene normalization problem by employing a carefully crafted suite of symbolic and statistical methods, and by fully relying on publicly available software and data resources, including extensive background knowledge based on semantic profiling. A major goal of our work is to present GeNo's architecture in a lucid and perspicuous way to pave the way to full reproducibility of our results. AVAILABILITY: GeNo, including its underlying resources, will be available from www.julielab.de. It is also currently deployed in the Semedico search engine at www.semedico.org.
Authors: Emily Doughty; Attila Kertesz-Farkas; Olivier Bodenreider; Gary Thompson; Asa Adadey; Thomas Peterson; Maricel G Kann Journal: Bioinformatics Date: 2010-12-07 Impact factor: 6.937