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

Open Access Report

Exploring the boundaries: gene and protein identification in biomedical text

Jenny Finkel1, Shipra Dingare2, Christopher D Manning1*, Malvina Nissim2, Beatrice Alex2 and Claire Grover2

Author affiliations

1 Department of Computer Science, Stanford University, Stanford CA 94305-9040, USA

2 Institute for Communicating and Collaborative Systems, University of Edinburgh, United Kingdom

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Citation and License

BMC Bioinformatics 2005, 6(Suppl 1):S5  doi:10.1186/1471-2105-6-S1-S5

Published: 24 May 2005



Good automatic information extraction tools offer hope for automatic processing of the exploding biomedical literature, and successful named entity recognition is a key component for such tools.


We present a maximum-entropy based system incorporating a diverse set of features for identifying gene and protein names in biomedical abstracts.


This system was entered in the BioCreative comparative evaluation and achieved a precision of 0.83 and recall of 0.84 in the "open" evaluation and a precision of 0.78 and recall of 0.85 in the "closed" evaluation.


Central contributions are rich use of features derived from the training data at multiple levels of granularity, a focus on correctly identifying entity boundaries, and the innovative use of several external knowledge sources including full MEDLINE abstracts and web searches.