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Metabolic Pathway Mining.

Jan M Czarnecki1, Adrian J Shepherd2

  • 1School of Biosciences, Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK.

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|November 30, 2016
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Summary
This summary is machine-generated.

Automated text-mining can help bioscience researchers curate metabolic pathways. This study presents a new pipeline to extract metabolic reactions from scientific literature, addressing a gap in current databases.

Keywords:
Information extractionMetabolic interaction extractionMetabolic pathwayNamed entity recognitionNatural language processingText-mining

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Area of Science:

  • Metabolic pathway research in the post-genomic era.
  • Biosciences and bioinformatics.
  • Computational biology and text mining applications.

Background:

  • Curation of metabolic pathways is labor-intensive and time-consuming.
  • Existing databases lack sufficient experimentally verified metabolic pathways.
  • Text-mining tools for biomedical networks have not focused on metabolic pathways.

Purpose of the Study:

  • To address the gap in automated metabolic pathway curation from literature.
  • To develop and describe a computational pipeline for extracting metabolic pathways.
  • To utilize text-mining for efficient discovery of metabolic reactions.

Main Methods:

  • Development of a novel pipeline for metabolic pathway extraction.
  • Utilizing freely available, open-source software components.
  • Implementation of a heuristic algorithm for metabolic reaction extraction from text.

Main Results:

  • A functional pipeline for extracting metabolic pathways has been established.
  • The pipeline leverages open-source tools for accessibility and reproducibility.
  • A heuristic algorithm effectively identifies metabolic reactions within literature.

Conclusions:

  • Text-mining offers a viable solution to accelerate metabolic pathway curation.
  • The developed pipeline provides a foundation for automated discovery of metabolic information.
  • This approach can significantly enhance the completeness of metabolic pathway databases.