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Biological Information Extraction and Co-occurrence Analysis

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Biomedical Literature Mining

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1159))

Abstract

Nowadays, it is possible to identify terms corresponding to biological entities within passages in biomedical text corpora: critically, their potential relationships then need to be detected. These relationships are typically detected by co-occurrence analysis, revealing associations between bioentities through their coexistence in single sentences and/or entire abstracts. These associations implicitly define networks, whose nodes represent terms/bioentities/concepts being connected by relationship edges; edge weights might represent confidence for these semantic connections.

This chapter provides a review of current methods for co-occurrence analysis, focusing on data storage, analysis, and representation. We highlight scenarios of these approaches implemented by useful tools for information extraction and knowledge inference in the field of systems biology. We illustrate the practical utility of two online resources providing services of this type—namely, STRING and BioTextQuest—concluding with a discussion of current challenges and future perspectives in the field.

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Acknowledgments

The work was supported in part by the European Commission FP7 programme “Translational Potential” (TransPOT; EC contract number 285948).

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Authors and Affiliations

  1. Division of Basic Sciences, University of Crete Medical School, Heraklion, 71110, Greece

    Georgios A. Pavlopoulos & Ioannis Iliopoulos

  2. Bioinformatics Research Laboratory, Department of Biological Sciences, University of Cyprus, 20537, 1678, Nicosia, Cyprus

    Vasilis J. Promponas

  3. Institute of Applied Biosciences, Centre for Research & Technology, 361, GR-57001, Thessalonica, Greece

    Christos A. Ouzounis

  4. Donnelly Centre for Cellular & Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario, M5S 3E1, Canada

    Christos A. Ouzounis

  5. Chemical Process Engineering Research Institute, Centre for Research & Technology, 361, GR-57001, Thessalonica, Greece

    Christos A. Ouzounis

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  1. Georgios A. Pavlopoulos
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  2. Vasilis J. Promponas
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  3. Christos A. Ouzounis
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  4. Ioannis Iliopoulos
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Correspondence to Ioannis Iliopoulos .

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  1. GlaxoSmithKline, King of Prussia, Pennsylvania, USA

    Vinod D. Kumar

  2. GlaxoSmithKline, Hitchin, Hertfordshire, United Kingdom

    Hannah Jane Tipney

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Pavlopoulos, G.A., Promponas, V.J., Ouzounis, C.A., Iliopoulos, I. (2014). Biological Information Extraction and Co-occurrence Analysis. In: Kumar, V., Tipney, H. (eds) Biomedical Literature Mining. Methods in Molecular Biology, vol 1159. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-0709-0_5

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  • DOI: https://doi.org/10.1007/978-1-4939-0709-0_5

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-0708-3

  • Online ISBN: 978-1-4939-0709-0

  • eBook Packages: Springer Protocols

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