A text-mining system for extracting metabolic reactions from full-text articles
1 Department of Biological Sciences and Institute of Molecular and Structural Biology, Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK
2 Unilever R&D, Colworth Science Park, Sharnbrook, Bedfordshire, MK44 1LG, UK
BMC Bioinformatics 2012, 13:172 doi:10.1186/1471-2105-13-172Published: 23 July 2012
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.
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.
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.