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Information discovery from complementary literatures: Categorizing viruses as potential weapons

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  • Don R. Swanson
  • Neil R. Smalheiser
  • A. Bookstein

Abstract

Using novel informatics techniques to process the output of Medline searches, we have generated a list of viruses that may have the potential for development as weapons. Our findings are intended as a guide to the virus literature to support further studies that might then lead to appropriate defense and public health measures. This article stresses methods that are more generally relevant to information science. Initial Medline searches identified two kinds of virus literatures—the first concerning the genetic aspects of virulence, and the second concerning the transmission of viral diseases. Both literatures taken together are of central importance in identifying research relevant to the development of biological weapons. Yet, the two literatures had very few articles in common. We downloaded the Medline records for each of the two literatures and used a computer to extract all virus terms common to both. The fact that the resulting virus list includes most of an earlier independently published list of viruses considered by military experts to have the highest threat as potential biological weapons served as a test of the method; the test outcome showed a high degree of statistical significance, thus supporting an inference that the new viruses on the list share certain important characteristics with viruses of known biological warfare interest.

Suggested Citation

  • Don R. Swanson & Neil R. Smalheiser & A. Bookstein, 2001. "Information discovery from complementary literatures: Categorizing viruses as potential weapons," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 52(10), pages 797-812.
  • Handle: RePEc:bla:jamist:v:52:y:2001:i:10:p:797-812
    DOI: 10.1002/asi.1135
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    Cited by:

    1. Neil R. Smalheiser, 2012. "Literature-based discovery: Beyond the ABCs," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(2), pages 218-224, February.
    2. Ronald N. Kostoff, 2014. "Literature-related discovery: common factors for Parkinson’s Disease and Crohn’s Disease," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 623-657, September.
    3. Oded Netzer & Ronen Feldman & Jacob Goldenberg & Moshe Fresko, 2012. "Mine Your Own Business: Market-Structure Surveillance Through Text Mining," Marketing Science, INFORMS, vol. 31(3), pages 521-543, May.
    4. Ronald N. Kostoff & Stephen A. Morse, 2011. "Structure and infrastructure of infectious agent research literature: SARS," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 195-209, January.
    5. Yi Bu & Tian-yi Liu & Win-bin Huang, 2016. "MACA: a modified author co-citation analysis method combined with general descriptive metadata of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(1), pages 143-166, July.
    6. Bar-Ilan, Judit, 2008. "Informetrics at the beginning of the 21st century—A review," Journal of Informetrics, Elsevier, vol. 2(1), pages 1-52.

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