BACKGROUND: In the absence of consolidated pipelines to archive biological data 
electronically, information dispersed in the literature must be captured by 
manual annotation. Unfortunately, manual annotation is time consuming and the 
coverage of published interaction data is therefore far from complete. The use 
of text-mining tools to identify relevant publications and to assist in the 
initial information extraction could help to improve the efficiency of the 
curation process and, as a consequence, the database coverage of data available 
in the literature. The 2006 BioCreative competition was aimed at evaluating 
text-mining procedures in comparison with manual annotation of protein-protein 
interactions.
RESULTS: To aid the BioCreative protein-protein interaction task, IntAct and 
MINT (Molecular INTeraction) provided both the training and the test datasets. 
Data from both databases are comparable because they were curated according to 
the same standards. During the manual curation process, the major cause of data 
loss in mining the articles for information was ambiguity in the mapping of the 
gene names to stable UniProtKB database identifiers. It was also observed that 
most of the information about interactions was contained only within the 
full-text of the publication; hence, text mining of protein-protein interaction 
data will require the analysis of the full-text of the articles and cannot be 
restricted to the abstract.
CONCLUSION: The development of text-mining tools to extract protein-protein 
interaction information may increase the literature coverage achieved by manual 
curation. To support the text-mining community, databases will highlight those 
sentences within the articles that describe the interactions. These will supply 
data-miners with a high quality dataset for algorithm development. Furthermore, 
the dictionary of terms created by the BioCreative competitors could enrich the 
synonym list of the PSI-MI (Proteomics Standards Initiative-Molecular 
Interactions) controlled vocabulary, which is used by both databases to annotate 
their data content.