Journal
BMC BIOINFORMATICS
Volume 13, Issue -, Pages -Publisher
BIOMED CENTRAL LTD
DOI: 10.1186/1471-2105-13-172
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Funding
- BBSRC CASE studentship
- industry partner Unilever
- Biotechnology and Biological Sciences Research Council [BB/F018665/1, 986680] Funding Source: researchfish
- BBSRC [BB/F018665/1] Funding Source: UKRI
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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.
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