MOTIVATION: Identification and characterization of protein-protein interactions 
(PPIs) is one of the key aims in biological research. While previous research in 
text mining has made substantial progress in automatic PPI detection from 
literature, the need to improve the precision and recall of the process remains. 
More accurate PPI detection will also improve the ability to extract 
experimental data related to PPIs and provide multiple evidence for each 
interaction.
RESULTS: We developed an interaction detection method and explored the 
usefulness of various features in automatically identifying PPIs in text. The 
results show that our approach outperforms other systems using the AImed 
dataset. In the tests where our system achieves better precision with reduced 
recall, we discuss possible approaches for improvement. In addition to test 
datasets, we evaluated the performance on interactions from five human-curated 
databases-BIND, DIP, HPRD, IntAct and MINT-where our system consistently 
identified evidence for approximately 60% of interactions when both proteins 
appear in at least one sentence in the PubMed abstract. We then applied the 
system to extract articles from PubMed to annotate known, high-throughput and 
interologous interactions in I(2)D.
AVAILABILITY: The data and software are available at: http://www.cs.utoronto.ca/ 
approximately juris/data/BI09/.