BACKGROUND: 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.
RESULTS: 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.
CONCLUSION: 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.
AVAILABILITY: Our methods for gene, protein, and species identification, and 
extraction of protein-protein are available as part of the BioCreative Meta 
Services (BCMS), see http://bcms.bioinfo.cnio.es/.