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Toward discovery support systems: A replication, re‐examination, and extension of Swanson's work on literature‐based discovery of a connection between Raynaud's and fish oil

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  • Michael D. Gordon
  • Robert K. Lindsay

Abstract

Don R. Swanson has undertaken a program of research to use the published medical literature as a source of discoveries. We have attempted to replicate his discovery of a connection between Raynaud's disease and dietary fish oil, as well as develop computer‐based searching methods that could usefully support literature‐based discoveries. We have been successful in replicating Swanson's discovery and have developed a method of discovery support based on the complete text of MEDLINE records. From these, we compute statistics based both on the frequency of tokens within a literature and on the number of records containing various tokens. We discuss the use of these statistics, suggesting that token and record frequencies are good indicators of literatures profitably related to some source literature, and that relative record frequencies are useful in isolating literatures with the potential of containing a discovery. © 1996 John Wiley & Sons, Inc.

Suggested Citation

  • Michael D. Gordon & Robert K. Lindsay, 1996. "Toward discovery support systems: A replication, re‐examination, and extension of Swanson's work on literature‐based discovery of a connection between Raynaud's and fish oil," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(2), pages 116-128, February.
    • Handle: RePEc:bla:jamest:v:47:y:1996:i:2:p:116-128
    DOI: 10.1002/(SICI)1097-4571(199602)47:23.0.CO;2-1
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    Cited by:

    1. Lv, Yanhua & Ding, Ying & Song, Min & Duan, Zhiguang, 2018. "Topology-driven trend analysis for drug discovery," Journal of Informetrics, Elsevier, vol. 12(3), pages 893-905.
    2. Jeong, Yoo Kyung & Xie, Qing & Yan, Erjia & Song, Min, 2020. "Examining drug and side effect relation using author–entity pair bipartite networks," Journal of Informetrics, Elsevier, vol. 14(1).
    3. Christian Sternitzke, 2009. "Patents and publications as sources of novel and inventive knowledge," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(3), pages 551-561, June.
    4. Chaomei Chen & Min Song, 2019. "Visualizing a field of research: A methodology of systematic scientometric reviews," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-25, October.
    5. Andrej Kastrin & Dimitar Hristovski, 2021. "Scientometric analysis and knowledge mapping of literature-based discovery (1986–2020)," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1415-1451, February.
    6. Alan L. Porter & Alisa Kongthon & Jye-Chyi (JC) Lu, 2002. "Research profiling: Improving the literature review," Scientometrics, Springer;Akadémiai Kiadó, vol. 53(3), pages 351-370, March.
    7. Johannes Stegmann & Guenter Grohmann, 2003. "Hypothesis generation guided by co-word clustering," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(1), pages 111-135, January.
    8. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    9. Emil Hudomalj & Gaj Vidmar, 2003. "OLAP and bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 58(3), pages 609-622, November.
    10. Gamal Crichton & Simon Baker & Yufan Guo & Anna Korhonen, 2020. "Neural networks for open and closed Literature-based Discovery," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
    11. Guangyu Zou & Levent Yilmaz, 2011. "Dynamics of knowledge creation in global participatory science communities: open innovation communities from a network perspective," Computational and Mathematical Organization Theory, Springer, vol. 17(1), pages 35-58, March.

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