Database
Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens
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* Corresponding author: Sam Zaremba Sam_Zaremba@sra.com
1 ERIC-BRC, SRA International Inc, Global Health Sector, Rockville MD, 20852, USA
2 14026 Marblestone Drive Clifton, VA 20124, USA
3 Genome Center, University of Wisconsin, Madison WI, 53706, USA
4 Laboratory of Genetics, University of Wisconsin, Madison WI, 53706, USA
BMC Bioinformatics 2009, 10:177 doi:10.1186/1471-2105-10-177
Published: 10 June 2009Abstract
Background
The Enteropathogen Resource Integration Center (ERIC; http://www.ericbrc.org webcite) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as Escherichia coli and Salmonella spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process.
Description
We have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc.) and over 70% for relations (gene/gene product to role, etc). This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application.
Conclusion
Our Text Mining application is available online on the ERIC website http://www.ericbrc.org/portal/eric/articles webcite. The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations systems.