BioContext: an integrated text mining system for large-scale extraction and contextualization of biomolecular events.
BACKGROUND
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.


BACKGROUND
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.
Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK. martin.gerner@gmail.com
Martin
M
Gerner
Farzaneh
F
Sarafraz
Casey M
CM
Bergman
Goran
G
Nenadic
D001669:Biochemical Phenomena; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D016239:MEDLINE; D039781:PubMed; D012984:Software