This article is part of the supplement: The BioCreative II - Critical Assessment for Information Extraction in Biology Challenge

Open Access Open Badges Research

Gene mention normalization and interaction extraction with context models and sentence motifs

Jörg Hakenberg123*, Conrad Plake14, Loic Royer1, Hendrik Strobelt1, Ulf Leser2 and Michael Schroeder1

Author Affiliations

1 Bioinformatics Group, Biotechnological Centre, Technische Universität Dresden, Tatzberg, 01307 Dresden, Germany

2 Knowledge Management in Bioinformatics, Computer Science Department, Humboldt-Universität zu Berlin, Unter den Linden, 10099 Berlin, Germany

3 Current affiliation: BioAI Lab, Arizona State University, S Mill Avenue, Tempe/Phoenix, Arizona 85281-8809, USA

4 Transinsight GmbH, Tatzberg, 01307 Dresden, Germany

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Genome Biology 2008, 9(Suppl 2):S14  doi:10.1186/gb-2008-9-s2-s14

Published: 1 September 2008



The goal of text mining is to make the information conveyed in scientific publications accessible to structured search and automatic analysis. Two important subtasks of text mining are entity mention normalization - to identify biomedical objects in text - and extraction of qualified relationships between those objects. We describe a method for identifying genes and relationships between proteins.


We present solutions to gene mention normalization and extraction of protein-protein interactions. For the first task, we identify genes by using background knowledge on each gene, namely annotations related to function, location, disease, and so on. Our approach currently achieves an f-measure of 86.4% on the BioCreative II gene normalization data. For the extraction of protein-protein interactions, we pursue an approach that builds on classical sequence analysis: motifs derived from multiple sequence alignments. The method achieves an f-measure of 24.4% (micro-average) in the BioCreative II interaction pair subtask.


For gene mention normalization, our approach outperforms strategies that utilize only the matching of genes names against dictionaries, without invoking further knowledge on each gene. Motifs derived from alignments of sentences are successful at identifying protein interactions in text; the approach we present in this report is fully automated and performs similarly to systems that require human intervention at one or more stages.


Our methods for gene, protein, and species identification, and extraction of protein-protein are available as part of the BioCreative Meta Services (BCMS), see webcite.