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This article is part of the supplement: A critical assessment of text mining methods in molecular biology

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Automatically annotating documents with normalized gene lists

Jeremiah Crim, Ryan McDonald* and Fernando Pereira

Author Affiliations

Department of Computer and Information Science, University of Pennsylvania, Levine Hall, 3330 Walnut Street, Philadelphia, Pennsylvania, USA, 19104

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BMC Bioinformatics 2005, 6(Suppl 1):S13  doi:10.1186/1471-2105-6-S1-S13

Published: 24 May 2005



Document gene normalization is the problem of creating a list of unique identifiers for genes that are mentioned within a document. Automating this process has many potential applications in both information extraction and database curation systems. Here we present two separate solutions to this problem. The first is primarily based on standard pattern matching and information extraction techniques. The second and more novel solution uses a statistical classifier to recognize valid gene matches from a list of known gene synonyms.


We compare the results of the two systems, analyze their merits and argue that the classification based system is preferable for many reasons including performance, simplicity and robustness. Our best systems attain a balanced precision and recall in the range of 74%–92%, depending on the organism.