BACKGROUND: Reliable information extraction applications have been a long sought 
goal of the biomedical text mining community, a goal that if reached would 
provide valuable tools to benchside biologists in their increasingly difficult 
task of assimilating the knowledge contained in the biomedical literature. We 
present an integrated approach to concept recognition in biomedical text. 
Concept recognition provides key information that has been largely missing from 
previous biomedical information extraction efforts, namely direct links to well 
defined knowledge resources that explicitly cement the concept's semantics. The 
BioCreative II tasks discussed in this special issue have provided a unique 
opportunity to demonstrate the effectiveness of concept recognition in the field 
of biomedical language processing.
RESULTS: Through the modular construction of a protein interaction relation 
extraction system, we present several use cases of concept recognition in 
biomedical text, and relate these use cases to potential uses by the benchside 
biologist.
CONCLUSION: Current information extraction technologies are approaching 
performance standards at which concept recognition can begin to deliver high 
quality data to the benchside biologist. Our system is available as part of the 
BioCreative Meta-Server project and on the internet 
http://bionlp.sourceforge.net.