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BMC Bioinformatics. 2012 Jul 23;13:172. doi: 10.1186/1471-2105-13-172.

A text-mining system for extracting metabolic reactions from full-text articles.

Author information

1
Department of Biological Sciences and Institute of Molecular and Structural Biology, Birkbeck, University of London, London, UK.

Abstract

BACKGROUND:

Increasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways. However, one important category of pathway - metabolic pathways - has been largely neglected.Here we present a relatively simple method for extracting metabolic reaction information from free text that scores different permutations of assigned entities (enzymes and metabolites) within a given sentence based on the presence and location of stemmed keywords. This method extends an approach that has proved effective in the context of the extraction of protein-protein interactions.

RESULTS:

When evaluated on a set of manually-curated metabolic pathways using standard performance criteria, our method performs surprisingly well. Precision and recall rates are comparable to those previously achieved for the well-known protein-protein interaction extraction task.

CONCLUSIONS:

We conclude that automated metabolic pathway construction is more tractable than has often been assumed, and that (as in the case of protein-protein interaction extraction) relatively simple text-mining approaches can prove surprisingly effective. It is hoped that these results will provide an impetus to further research and act as a useful benchmark for judging the performance of more sophisticated methods that are yet to be developed.

PMID:
22823282
PMCID:
PMC3475109
DOI:
10.1186/1471-2105-13-172
[Indexed for MEDLINE]
Free PMC Article

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