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Updated: Jul 14, 2026

Interactome-Seq: A Protocol for Domainome Library Construction, Validation and Selection by Phage Display and Next Generation Sequencing
Published on: October 3, 2018
Exploring the boundaries: gene and protein identification in biomedical text
Jenny Finkel1, Shipra Dingare, Christopher D Manning
1Department of Computer Science, Stanford University, Stanford, CA 94305-9040, USA. jrfinkel@stanford.edu
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.
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