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

• Main Authors: Czarnecki Jan, Nobeli Irene, Smith Adrian M, Shepherd Adrian J
• Format: Article
• Language: English
• Published: BMC 2012-07-01
• Series: BMC Bioinformatics
• Online Access: http://www.biomedcentral.com/1471-2105/13/172
• ISSN: 1471-2105

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p>