ArticleBMC bioinformatics2012
A text-mining system for extracting metabolic reactions from full-text articles.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- EnzChemRED, a rich enzyme chemistry relation extraction dataset.Scientific data · 2024Article
- Overview of DrugProt task at BioCreative VII: data and methods for large-scale text mining and knowledge graph generation of heterogenous chemical-protein relations.Database : the journal of biological databases and curation · 2023Article
- Article
- Integrating bioinformatics approaches for a comprehensive interpretation of metabolomics datasets.Current opinion in biotechnology · 2018Review
- A Review of Recent Advancement in Integrating Omics Data with Literature Mining towards Biomedical Discoveries.International journal of genomics · 2017Review
- Microbial phenomics information extractor (MicroPIE): a natural language processing tool for the automated acquisition of prokaryotic phenotypic characters from text sources.BMC bioinformatics · 2016Article
- Weakly supervised learning of biomedical information extraction from curated data.BMC bioinformatics · 2016Article
- Article
- Large-scale extraction of gene interactions from full-text literature using DeepDive.Bioinformatics (Oxford, England) · 2016Article
- Text Mining for Protein Docking.PLoS computational biology · 2015Article
- Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery.Drug discovery today · 2014Review
- Detecting miRNA Mentions and Relations in Biomedical Literature.F1000Research · 2014Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundIncreasingly 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.
resultsWhen 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.
conclusionsWe 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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Registered trials
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