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Bioinformatics pp 139–158Cite as

Metabolic Pathway Mining

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1526))

Abstract

Understanding metabolic pathways is one of the most important fields in bioscience in the post-genomic era, but curating metabolic pathways requires considerable man-power. As such there is a lack of reliable, experimentally verified metabolic pathways in databases and databases are forced to predict all but the most immediately useful pathways.

Text-mining has the potential to solve this problem, but while sophisticated text-mining methods have been developed to assist the curation of many types of biomedical networks, such as protein–protein interaction networks, the mining of metabolic pathways from the literature has been largely neglected by the text-mining community. In this chapter we describe a pipeline for the extraction of metabolic pathways built on freely available open-source components and a heuristic metabolic reaction extraction algorithm.

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Abbreviations

NER :

Named entity recognition

NLP :

Natural language processing

PPI :

Protein–protein interaction

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Author information

Authors and Affiliations

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

    Jan M. Czarnecki

  2. Department of Biological Sciences and Institute of Structural and Molecular Biology, Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK

    Adrian J. Shepherd

Authors
  1. Jan M. Czarnecki
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  2. Adrian J. Shepherd
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Corresponding author

Correspondence to Adrian J. Shepherd .

Editor information

Editors and Affiliations

  1. Monash University, Melbourne, Victoria, Australia

    Jonathan M. Keith

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Czarnecki, J.M., Shepherd, A.J. (2017). Metabolic Pathway Mining. In: Keith, J. (eds) Bioinformatics. Methods in Molecular Biology, vol 1526. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6613-4_8

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  • DOI: https://doi.org/10.1007/978-1-4939-6613-4_8

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-6611-0

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