Concept recognition for extracting protein interaction relations from biomedical text.
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.


CONCLUSIONS
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.
Baumgartner
William A
WA
Center for Computational Pharmacology, University of Colorado School of Medicine, Aurora, Colorado 80045, USA.
Lu
Zhiyong
Z
Johnson
Helen L
HL
Caporaso
J Gregory
JG
Paquette
Jesse
J
Lindemann
Anna
A
White
Elizabeth K
EK
Medvedeva
Olga
O
Cohen
K Bretonnel
KB
Hunter
Lawrence
L
D035843:Biomedical Research; D016206:Databases, Bibliographic; D005796:Genes; D016247:Information Storage and Retrieval; D010363:Pattern Recognition, Automated; D025941:Protein Interaction Mapping