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

Open Access Report

ProMiner: rule-based protein and gene entity recognition

Daniel Hanisch12, Katrin Fundel3, Heinz-Theodor Mevissen1, Ralf Zimmer3 and Juliane Fluck1*

Author Affiliations

1 Fraunhofer Institute SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany

2 Current address: Aventis Pharma Deutschland, Industriepark Hoechst G879, 65926 Frankfurt am Main, Germany

3 Institute for Informatics, Ludwig-Maximilians-Universität München, Amalienstrasse 17, 80333 München, Germany

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BMC Bioinformatics 2005, 6(Suppl 1):S14  doi:10.1186/1471-2105-6-S1-S14

Published: 24 May 2005



Identification of gene and protein names in biomedical text is a challenging task as the corresponding nomenclature has evolved over time. This has led to multiple synonyms for individual genes and proteins, as well as names that may be ambiguous with other gene names or with general English words. The Gene List Task of the BioCreAtIvE challenge evaluation enables comparison of systems addressing the problem of protein and gene name identification on common benchmark data.


The ProMiner system uses a pre-processed synonym dictionary to identify potential name occurrences in the biomedical text and associate protein and gene database identifiers with the detected matches. It follows a rule-based approach and its search algorithm is geared towards recognition of multi-word names [1]. To account for the large number of ambiguous synonyms in the considered organisms, the system has been extended to use specific variants of the detection procedure for highly ambiguous and case-sensitive synonyms. Based on all detected synonyms for one abstract, the most plausible database identifiers are associated with the text. Organism specificity is addressed by a simple procedure based on additionally detected organism names in an abstract.


The extended ProMiner system has been applied to the test cases of the BioCreAtIvE competition with highly encouraging results. In blind predictions, the system achieved an F-measure of approximately 0.8 for the organisms mouse and fly and about 0.9 for the organism yeast.