BACKGROUND: Genome sciences have experienced an increasing demand for efficient 
text-processing tools that can extract biologically relevant information from 
the growing amount of published literature. In response, a range of text-mining 
and information-extraction tools have recently been developed specifically for 
the biological domain. Such tools are only useful if they are designed to meet 
real-life tasks and if their performance can be estimated and compared. The 
BioCreative challenge (Critical Assessment of Information Extraction in Biology) 
consists of a collaborative initiative to provide a common evaluation framework 
for monitoring and assessing the state-of-the-art of text-mining systems applied 
to biologically relevant problems.
RESULTS: The Second BioCreative assessment (2006 to 2007) attracted 44 teams 
from 13 countries worldwide, with the aim of evaluating current 
information-extraction/text-mining technologies developed for one or more of the 
three tasks defined for this challenge evaluation. These tasks included the 
recognition of gene mentions in abstracts (gene mention task); the extraction of 
a list of unique identifiers for human genes mentioned in abstracts (gene 
normalization task); and finally the extraction of physical protein-protein 
interaction annotation-relevant information (protein-protein interaction task). 
The 'gold standard' data used for evaluating submissions for the third task was 
provided by the interaction databases MINT (Molecular Interaction Database) and 
IntAct.
CONCLUSION: The Second BioCreative assessment almost doubled the number of 
participants for each individual task when compared with the first BioCreative 
assessment. An overall improvement in terms of balanced precision and recall was 
observed for the best submissions for the gene mention (F score 0.87); for the 
gene normalization task, the best results were comparable (F score 0.81) 
compared with results obtained for similar tasks posed at the first BioCreative 
challenge. In case of the protein-protein interaction task, the importance and 
difficulties of experimentally confirmed annotation extraction from full-text 
articles were explored, yielding different results depending on the step of the 
annotation extraction workflow. A common characteristic observed in all three 
tasks was that the combination of system outputs could yield better results than 
any single system. Finally, the development of the first text-mining meta-server 
was promoted within the context of this community challenge.