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International Conference on Data Analytics and Management in Data Intensive Domains

DAMDID/RCDL 2016: Data Analytics and Management in Data Intensive Domains pp 59–73Cite as

Supporting Biological Pathway Curation Through Text Mining

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 706))

Abstract

Text mining technology performs automated analysis of large document collections, in order to detect various aspects of information about their structure and meaning. This information can be used to develop systems that make it much easier for researchers to locate information of relevance to their needs in huge volumes of text, compared to standard search mechanisms. With a focus on the challenging task of constructing biological pathway models, which typically involves gathering, interpreting and combining complex information from a large number of publications, we show how text mining applications can provide various levels of support to ease the burden placed on pathway curators. Such support ranges from applications that provide help in searching and exploring the literature for evidence relevant to pathway reactions, to those which are able to make automated suggestions about how to construct and update pathway models.

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Notes

  1. 1.

    http://www.nactem.ac.uk/Kleio/.

  2. 2.

    http://www.nactem.ac.uk/facta/.

  3. 3.

    http://www.nactem.ac.uk/facta-visualizer/.

  4. 4.

    http://www.nactem.ac.uk/medie/.

  5. 5.

    http://www.nactem.ac.uk/big_mechanism/.

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Acknowledgements

The work described in this article has been supported by the BBSRC-funded EMPATHY project (Grant No. BB/M006891/1) and by the DARPA-funded Big Mechanism project Grant No. DARPA-BAA-14-14).

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Authors and Affiliations

  1. School of Computer Science, National Centre for Text Mining, University of Manchester, Manchester, UK

    Sophia Ananiadou & Paul Thompson

Authors
  1. Sophia Ananiadou
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  2. Paul Thompson
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Corresponding author

Correspondence to Sophia Ananiadou .

Editor information

Editors and Affiliations

  1. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Leonid Kalinichenko

  2. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  3. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Yannis Manolopoulos

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Ananiadou, S., Thompson, P. (2017). Supporting Biological Pathway Curation Through Text Mining. In: Kalinichenko, L., Kuznetsov, S., Manolopoulos, Y. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2016. Communications in Computer and Information Science, vol 706. Springer, Cham. https://doi.org/10.1007/978-3-319-57135-5_5

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  • DOI: https://doi.org/10.1007/978-3-319-57135-5_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57134-8

  • Online ISBN: 978-3-319-57135-5

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