Mining Protein–Protein Interactions from Published Literature Using Linguamatics I2E

  • Judith Bandy
  • David Milward
  • Sarah McQuay
Part of the Methods in Molecular Biology book series (MIMB, volume 563)


Natural language processing (NLP) technology can be used to rapidly extract protein–protein interactions from large collections of published literature. In this chapter we will work through a case study using MEDLINE® biomedical abstracts (1) to find how a specific set of 50 genes interact with each other. We will show what steps are required to achieve this using the I2E software from Linguamatics ( (2)).

To extract protein networks from the literature, there are two typical strategies. The first is to find pairs of proteins which are mentioned together in the same context, for example, the same sentence, with the assumption that textual proximity implies biological association. The second approach is to use precise linguistic patterns based on NLP to find specific relationships between proteins. This can reveal the direction of the relationship and its nature such as “phosphorylation” or “upregulation”. The I2E system uses a flexible text-mining approach, supporting both of these strategies, as well as hybrid strategies which fall between the two. In this chapter we show how multiple strategies can be combined to obtain high-quality results.

Key words

Protein–protein interactions text mining natural language processing NLP knowledge discovery information extraction linguistics literature MEDLINE Linguamatics I2E 


  1. 1.
    MEDLINE® (Medical Literature Analysis and Retrieval System Online) is the U.S. National Library of Medicine’s® (NLM) premier bibliographic database that contains over 17 million references to journal articles in life sciences with a concentration on biomedicine (
  2. 2.
    I2E is developed and marketed by Linguamatics Ltd. Further information can be obtained from or by contacting the contributing authors.
  3. 3.
    Milward, D., Blaschke, C., Neefs, J.-M., Ott, M.-C., Verbeeck, R., and Stubbs, A. (2006) Flexible Text Mining Strategies for Drug Discovery. Proc. Second International Symposium on Semantic Mining in BioMedicine (SMBM 2006), Jena, Germany April 9–12, 2006 pp. 101–104.Google Scholar
  4. 4.
    Thomas, J., Milward, D., Ouzounis, C., Pulman, S., and Carroll, M. (2000) Automatic extraction of protein interactions from scientific abstracts. Pac. Symp. Biocomput., Waikiki, Hawaii, 2000 January 4–9 541–552.Google Scholar
  5. 5.
    Humphreys, K., Demetriou, G., and Geizauskas, R. (2000) Two applications of information extraction to biological science journal articles: Enzyme interactions and protein structure. Pac. Symp. Biocomput., Waikiki, Hawaii, 2000 January 4–9 502–513.Google Scholar
  6. 6.
    Milward, D., Bjäreland, M., Hayes, W., Maxwell, M., Öberg, L., Tilford, N., Thomas, J., Hale, R., Knight, S., and Barnes, J. (2005) Ontology-based interactive information extraction from scientific abstracts. Comp. Funct. Genomics, 6, 67–71.PubMedCrossRefGoogle Scholar
  7. 7.
    HUGO, The Human Gene Organization,
  8. 8.
    Maglott, D., Ostell, J., Pruitt, K.D., and Tatusova, T. (2005) Entrez Gene: Gene-centered information at NCBI. Nucleic Acids Res., 33, D54–D58.PubMedCrossRefGoogle Scholar
  9. 9.
    Hearst, M.A. (1999) Untangling Text Data Mining. Proc. 37th Annual Meeting of the Association for Computational Linguistics, University of Maryland, College Park. June 20–26, 1999.Google Scholar
  10. 10.
    Shannon, P., Markiel, A., Ozier, O., Baliga, N.S., Wang, J.T., Ramage, D., Amin, N., Schwikowski, B., and Ideker, T. (2003) Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res., 13, 2498–2504. Google Scholar
  11. 11.
    The InforSense Platform is developed and marketed by InforSense Ltd. Further information can be obtained from

Copyright information

© Humana Press, a part of Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Judith Bandy
    • 1
  • David Milward
    • 2
  • Sarah McQuay
    • 2
  1. 1.Linguamatics Ltd, St John’s Innovation CentreCambridgeUK
  2. 2.Linguamatics LtdCambridgeUK

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