MOTIVATION: The ambiguity of biomedical entities, particularly of gene symbols, is a big challenge for text-mining systems in the biomedical domain. Existing knowledge sources, such as Entrez Gene and the MEDLINE database, contain information concerning the characteristics of a particular gene that could be used to disambiguate gene symbols. RESULTS: For each gene, we create a profile with different types of information automatically extracted from related MEDLINE abstracts and readily available annotated knowledge sources. We apply the gene profiles to the disambiguation task via an information retrieval method, which ranks the similarity scores between the context where the ambiguous gene is mentioned, and candidate gene profiles. The gene profile with the highest similarity score is then chosen as the correct sense. We evaluated the method on three automatically generated testing sets of mouse, fly and yeast organisms, respectively. The method achieved the highest precision of 93.9% for the mouse, 77.8% for the fly and 89.5% for the yeast. AVAILABILITY: The testing data sets and disambiguation programs are available at http://www.dbmi.columbia.edu/~hux7002/gsd2006
MOTIVATION: The ambiguity of biomedical entities, particularly of gene symbols, is a big challenge for text-mining systems in the biomedical domain. Existing knowledge sources, such as Entrez Gene and the MEDLINE database, contain information concerning the characteristics of a particular gene that could be used to disambiguate gene symbols. RESULTS: For each gene, we create a profile with different types of information automatically extracted from related MEDLINE abstracts and readily available annotated knowledge sources. We apply the gene profiles to the disambiguation task via an information retrieval method, which ranks the similarity scores between the context where the ambiguous gene is mentioned, and candidate gene profiles. The gene profile with the highest similarity score is then chosen as the correct sense. We evaluated the method on three automatically generated testing sets of mouse, fly and yeast organisms, respectively. The method achieved the highest precision of 93.9% for the mouse, 77.8% for the fly and 89.5% for the yeast. AVAILABILITY: The testing data sets and disambiguation programs are available at http://www.dbmi.columbia.edu/~hux7002/gsd2006
Authors: Karin Verspoor; Christophe Roeder; Helen L Johnson; K Bretonnel Cohen; William A Baumgartner; Lawrence E Hunter Journal: IEEE/ACM Trans Comput Biol Bioinform Date: 2010 Jul-Sep Impact factor: 3.710
Authors: S Pakhomov; B T McInnes; J Lamba; Y Liu; G B Melton; Y Ghodke; N Bhise; V Lamba; A K Birnbaum Journal: J Biomed Inform Date: 2012-05-04 Impact factor: 6.317
Authors: Yonghui Wu; Mia A Levy; Christine M Micheel; Paul Yeh; Buzhou Tang; Michael J Cantrell; Stacy M Cooreman; Hua Xu Journal: BMC Genomics Date: 2012-12-17 Impact factor: 3.969