Combining multiple evidence for gene symbol disambiguation

Authors:
Hua Xu;Jung-Wei Fan;Carol Friedman
Affiliations:
Columbia University, St. NY;Columbia University, St. NY;Columbia University, St. NY
Venue:
BioNLP '07 Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing
Year:
2007

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Abstract

Gene names and symbols are important biomedical entities, but are highly ambiguous. This ambiguity affects the performance of both information extraction and information retrieval systems in the biomedical domain. Existing knowledge sources contain different types of information about genes and could be used to disambiguate gene symbols. In this paper, we applied an information retrieval (IR) based method for human gene symbol disambiguation and studied different methods to combine various types of information from available knowledge sources. Results showed that a combination of evidence usually improved performance. The combination method using coefficients obtained from a logistic regression model reached the highest precision of 92.2% on a testing set of ambiguous human gene symbols.