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

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Exploring the boundaries: gene and protein identification in biomedical text

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

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

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

author email corresponding author email

BMC Bioinformatics 2005, 6(Suppl 1):S5doi:10.1186/1471-2105-6-S1-S5

Published:	24 May 2005

Abstract

Background

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.

Methods

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

Results

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.

Conclusion

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.