Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens
The Enteropathogen Resource Integration Center (ERIC; www.ericbrc.org) 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: ERIC has 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 ERIC's recently deployed text mining application.
Conclusions: The ERIC Text Mining application was recently launched online on the ERIC website (http://www.ericbrc.org/portal/eric/articles).
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.
Author: Sam ZarembaMila Ramos-SantacruzThomas HamptonPanna ShettyJoel FedorkoJon WhitmoreJohn GreeneNicole PernaJeremy GlasnerGuy PlunkettMatthew ShakerDavid Pot
Credits/Source: BMC Bioinformatics 2009, 10:177
Published on: 2009-06-10
Copyright by the authors listed above - made available via BioMedCentral (Open Access). Please
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