BACKGROUND: Research scientists and companies working in the domains of 
biomedicine and genomics are increasingly faced with the problem of efficiently 
locating, within the vast body of published scientific findings, the critical 
pieces of information that are needed to direct current and future research 
investment.
RESULTS: In this report we describe approaches taken within the scope of the 
second BioCreative competition in order to solve two aspects of this problem: 
detection of novel protein interactions reported in scientific articles, and 
detection of the experimental method that was used to confirm the interaction. 
Our approach to the former problem is based on a high-recall protein annotation 
step, followed by two strict disambiguation steps. The remaining proteins are 
then combined according to a number of lexico-syntactic filters, which deliver 
high-precision results while maintaining reasonable recall. The detection of the 
experimental methods is tackled by a pattern matching approach, which has 
delivered the best results in the official BioCreative evaluation.
CONCLUSION: Although the results of BioCreative clearly show that no tool is 
sufficiently reliable for fully automated annotations, a few of the proposed 
approaches (including our own) already perform at a competitive level. This 
makes them interesting either as standalone tools for preliminary document 
inspection, or as modules within an environment aimed at supporting the process 
of curation of biomedical literature.