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

Czarnecki J, Nobeli I, Smith AM, Shepherd AJ - BMC Bioinformatics (2012)

Bottom Line: Increasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: 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.

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The pantothenate and coenzyme a biosynthesis pathway. A diagram of the pathway obtained using the BioCyc pathway viewer[35].
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Figure 1: The pantothenate and coenzyme a biosynthesis pathway. A diagram of the pathway obtained using the BioCyc pathway viewer[35].

Mentions: We chose three pathways from EcoCyc and collected the original papers cited in each of these EcoCyc entries: the pantothenate and coenzyme A biosynthesis pathway (8 papers), shown in Figure1; and the tetrahydrofolate biosynthesis pathway (13 papers) and the aerobic fatty acid β-oxidation I pathway (11 papers), shown in Additional file2. All three pathways are from E. coli K-12 substr. MG1655. All reactions in all three pathways have at least one substrate, product and enzyme; some reactions have multiple substrates and/or products, but there is never more than one enzyme.


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

Czarnecki J, Nobeli I, Smith AM, Shepherd AJ - BMC Bioinformatics (2012)

The pantothenate and coenzyme a biosynthesis pathway. A diagram of the pathway obtained using the BioCyc pathway viewer[35].
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3475109&req=5

Figure 1: The pantothenate and coenzyme a biosynthesis pathway. A diagram of the pathway obtained using the BioCyc pathway viewer[35].
Mentions: We chose three pathways from EcoCyc and collected the original papers cited in each of these EcoCyc entries: the pantothenate and coenzyme A biosynthesis pathway (8 papers), shown in Figure1; and the tetrahydrofolate biosynthesis pathway (13 papers) and the aerobic fatty acid β-oxidation I pathway (11 papers), shown in Additional file2. All three pathways are from E. coli K-12 substr. MG1655. All reactions in all three pathways have at least one substrate, product and enzyme; some reactions have multiple substrates and/or products, but there is never more than one enzyme.

Bottom Line: Increasingly biological text mining research is focusing on the extraction of complex relationships relevant to the construction and curation of biological networks and pathways.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: 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.

Show MeSH