Exploring the boundaries: gene and protein identification in biomedical text

Abstract

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.</p