Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Text Mining of Biological Resources

  • Padmini SrinivasanEmail author
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_635


Hypothesis generation and exploration from biological resources; Knowledge discovery from biological resources; Literature-based discovery from biological resources


Text mining is about automatically or semiautomatically exploring hypotheses or new ideas from a set of resources. The mined hypotheses require further tests with methods native to the discipline, in this case with scientific methods in biomedicine. An overall goal in text mining is to support the intellectual activities of biomedical scientists as they explore new ideas using a collection of resources. Text mining is similar to data mining. But instead of mining a collection of well-structured data, text mining operates off semi-structured text collections. Current text mining efforts in biomedicine increasingly involve more structured data sources such as the Entrez Gene database maintained by the National Library of Medicine (NLM).

There is some diversity of opinion on the kindsof research that fall...

This is a preview of subscription content, log in to check access.

Recommended Reading

  1. 1.
    Blagosklonny MV, Pardee AB. Unearthing the gems. Nature. 2002;416(6879):373.CrossRefGoogle Scholar
  2. 2.
    Database of Interacting Proteins: http://dip.doe-mbi.ucla.edu/
  3. 3.
  4. 4.
  5. 5.
    Gordon MD, Lindsay RK. Toward discovery support systems: a replication, reexamination, and extension of Swanson’s work on literature-based discovery of a connection between Raynaud’s and fish oil. J Am Soc Inf Sci. 1996;47(2):116–28.CrossRefGoogle Scholar
  6. 6.
  7. 7.
    Perez-Iratxeta C, Bork P, Andrade MA. Association of genes to genetically inherited diseases using data mining. Nat Genet. 2002;31(3):316–9.CrossRefGoogle Scholar
  8. 8.
  9. 9.
    Seki K, Mostafa J. Discovering implicit associations between genes and hereditary diseases. In: Proceedings of the 12th Pacific Symposium on Bio-computing; 2007. p. 316–27.Google Scholar
  10. 10.
    Smalheiser NR, Swanson DR. Linking estrogen to Alzheimer’s disease: an informatics approach. Neurology. 1996;47:809–10.CrossRefGoogle Scholar
  11. 11.
    Srinivasan P. Text mining: generating hypotheses from MEDLINE. J Am Soc Inf Sci Technol. 2004;55(5):396–413.CrossRefGoogle Scholar
  12. 12.
    Srinivasan P, Libbus B. Mining MEDLINE for implicit links between dietary substances and diseases. Bioinformatics. 2004;20(Suppl 1):I290–6.CrossRefGoogle Scholar
  13. 13.
    Swanson DR. Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspect Biol Med. 1986;30(1):7–18.CrossRefGoogle Scholar
  14. 14.
    Swanson DR, Smalheiser NR, Bookstein A. Information discovery from complementary literatures: categorizing viruses as potential weapons. J Am Soc Inf Sci Technol. 2001;52(10):797–812.CrossRefGoogle Scholar
  15. 15.
    Weeber M, Kors JA, Mons B. Online tools to support literature-based discovery in the life sciences. Brief Bioinform. 2005;6(3):277–86. https://doi.org/10.1093/bib/6.3.277.CrossRefGoogle Scholar
  16. 16.
    Weeber M, Vos R, Klein H, de Jong-Van den Berg LTW, Aronson A, Molema G. Generating hypotheses by discovering implicit associations in the literature: a case report for new potential therapeutic uses for thalidomide. J Am Med Inform Assoc. 2003;10(3):252–9.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.The University of IowaIowa CityUSA

Section editors and affiliations

  • Louiqa Raschid
    • 1
  1. 1.Robert H. Smith School of BusinessUniversity of MarylandCollege ParkUSA