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Literature-related discovery: common factors for Parkinson’s Disease and Crohn’s Disease

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  • Ronald N. Kostoff

    (The MITRE Corporation
    Georgia Institute of Technology)

Abstract

Literature-related discovery (LRD) is the linking of two or more literature concepts that have heretofore not been linked (i.e., disjoint), in order to produce novel, interesting, and intelligible knowledge (i.e., potential discovery). The mainstream software for assisting LRD is Arrowsmith. It uses text-based linkage to connect two disjoint literatures, and it generates intermediate linking literatures by matching Title phrases from two disjoint literatures (literatures that do not share common records). Arrowsmith then prioritizes these linking phrases through a series of text-based filters. The present study examines citation-based linkage in addition to text-based linkage to link disjoint literatures through a process called bibliographic coupling. Two disjoint literatures were selected for the demonstration: Parkinson’s Disease (PD) (neurodegeneration) and Crohn’s Disease (CD) (autoimmune). Three cases were examined: (1) matching phrases in records with no shared references (text-based linkage only); (2) shared references in records with no matching phrases (citation-based linkage only); (3) matching phrases in records with shared references (text-based and citation-based linkages). In addition, the main themes in the body of shared references were examined through grouping techniques to identify the common themes between the two literatures. All the high-level concepts in the Case 1) records could be found in Case 3) records Some new concepts (at the sub-set level of the main themes) not found in the Case 3) records were identified in the Case 2) records. The synergy of matching phrases and shared references provides a strong prioritization to the selection of promising matching phrases as discovery mechanisms. There were three major themes that unified the PD and CD literatures: Genetics; Neuroimmunology; Cell Death. However, these themes are not completely independent. For example, there are genetic determinants of the inflammatory response. Naturally occurring genetic variants in important inflammatory mediators such as TNF-alpha appear to alter inflammatory responses in numerous experimental and a few clinical models of inflammation. Additionally, there is a strong link between neuroimmunology and cell death. In PD, for example, neuroinflammatory processes that are mediated by activated glial and peripheral immune cells might eventually lead to dopaminergic cell death and subsequent disease progression.

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  • 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.
  • Handle: RePEc:spr:scient:v:100:y:2014:i:3:d:10.1007_s11192-014-1298-3
    DOI: 10.1007/s11192-014-1298-3
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    References listed on IDEAS

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    1. Xinhai Liu & Shi Yu & Frizo Janssens & Wolfgang Glänzel & Yves Moreau & Bart De Moor, 2010. "Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(6), pages 1105-1119, June.
    2. M. M. Kessler, 1963. "Bibliographic coupling between scientific papers," American Documentation, Wiley Blackwell, vol. 14(1), pages 10-25, January.
    3. Kevin W. Boyack & Richard Klavans, 2010. "Co‐citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    4. Xinhai Liu & Wolfgang Glänzel & Bart Moor, 2012. "Optimal and hierarchical clustering of large-scale hybrid networks for scientific mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 473-493, May.
    5. 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.
    6. Naoki Shibata & Yuya Kajikawa & Yoshiyuki Takeda & Katsumori Matsushima, 2009. "Comparative study on methods of detecting research fronts using different types of citation," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(3), pages 571-580, March.
    7. Kevin W. Boyack & Richard Klavans, 2010. "Co-citation analysis, bibliographic coupling, and direct citation: Which citation approach represents the research front most accurately?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(12), pages 2389-2404, December.
    8. Padmini Srinivasan, 2004. "Text mining: Generating hypotheses from MEDLINE," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 55(5), pages 396-413, March.
    9. Frizo Janssens & Wolfgang Glänzel & Bart Moor, 2008. "A hybrid mapping of information science," Scientometrics, Springer;Akadémiai Kiadó, vol. 75(3), pages 607-631, June.
    10. Jarneving, Bo, 2007. "Complete graphs and bibliographic coupling: A test of the applicability of bibliographic coupling for the identification of cognitive cores on the field level," Journal of Informetrics, Elsevier, vol. 1(4), pages 338-356.
    11. Don R. Swanson, 1987. "Two medical literatures that are logically but not bibliographically connected," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 38(4), pages 228-233, July.
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