MOTIVATION: Although the amount of data in biology is rapidly increasing, 
critical information for understanding biological events like phosphorylation or 
gene expression remains locked in the biomedical literature. Most current text 
mining (TM) approaches to extract information about biological events are 
focused on either limited-scale studies and/or abstracts, with data extracted 
lacking context and rarely available to support further research.
RESULTS: Here we present BioContext, an integrated TM system which extracts, 
extends and integrates results from a number of tools performing entity 
recognition, biomolecular event extraction and contextualization. Application of 
our system to 10.9 million MEDLINE abstracts and 234 000 open-access full-text 
articles from PubMed Central yielded over 36 million mentions representing 11.4 
million distinct events. Event participants included over 290 000 distinct 
genes/proteins that are mentioned more than 80 million times and linked where 
possible to Entrez Gene identifiers. Over a third of events contain contextual 
information such as the anatomical location of the event occurrence or whether 
the event is reported as negated or speculative.
AVAILABILITY: The BioContext pipeline is available for download (under the BSD 
license) at http://www.biocontext.org, along with the extracted data which is 
also available for online browsing.