Paper Info
Title
BioContext: an integrated text mining system for large-scale extraction and contextualization of biomolecular events.
Abstract
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.
Year
DOI
Venue
2012
10.1093/bioinformatics/bts332
BIOINFORMATICS
Keywords
Field
DocType
computational biology,data mining
Data mining,Contextual information,Text mining,Information retrieval,Identifier,Computer science,Entrez Gene,Bioinformatics,MEDLINE,Contextualization
Journal
Volume
Issue
ISSN
28
16
1367-4803
Citations 
PageRank 
References 
12
0.53
30
Authors
4
Name
Order
Citations
PageRank
Martin Gerner12329.98
Farzaneh Sarafraz2664.37
Casey M Bergman343233.52
Goran Nenadic422813.18