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Updated: Jul 14, 2026
12:04
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
BMC Bioinformatics
|June 18, 2005
Summary
This study introduces a new system for automatically identifying gene and protein names in biomedical literature. The system achieved high precision and recall in a competitive evaluation, aiding information extraction.
Area of Science:
- Biomedical informatics
- Natural Language Processing
- Computational Biology
Background:
- Automatic information extraction is crucial for processing the vast biomedical literature.
- Named Entity Recognition (NER) is a fundamental task for enabling automated literature analysis.
Purpose of the Study:
- To develop and evaluate a novel system for the automated identification of gene and protein names within biomedical abstracts.
- To improve the accuracy and efficiency of Named Entity Recognition in the biomedical domain.
Main Methods:
- A maximum-entropy based system was developed for gene and protein name recognition.
- The system incorporated a diverse set of features, including those derived from training data at multiple granularity levels.
- External knowledge sources, such as full MEDLINE abstracts and web searches, were integrated.
Main Results:
- The system demonstrated strong performance in the BioCreative comparative evaluation.
- Achieved a precision of 0.83 and recall of 0.84 in the 'open' evaluation setting.
- Obtained a precision of 0.78 and recall of 0.85 in the 'closed' evaluation setting.
Conclusions:
- Key contributions include the extensive use of multi-level features and a focus on accurate entity boundary detection.
- The innovative application of external knowledge sources enhanced the system's capabilities.
- The developed system represents a significant advancement in automated biomedical Named Entity Recognition.
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