Scientometrics

, Volume 100, Issue 3, pp 623–657 | Cite as

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

Article

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.

Keywords

Literature-related discovery Text mining Scientometrics Parkinson’s Disease Crohn’s Disease Neurodegeneration Autoimmunity Inflammation 

References

  1. Ahlskog, J. E. (2005). The Parkinson’s Disease treatment book: Partnering with your doctor to get the most from your medications (1st ed.). USA: Oxford University Press.Google Scholar
  2. Akobeng, A. K. (2008). Crohn’s disease: Current treatment options. Archives of Disease in Childhood, 93(9), 787–792.CrossRefGoogle Scholar
  3. Annese, V., Valvano, M. R., Palmieri, O., Latiano, A., Bossa, F., & Andriulli, A. (2006). Multidrug resistance 1 gene in inflammatory bowel disease: A meta-analysis. World Journal of Gastroenterology, 12(23), 3636–3644.Google Scholar
  4. Baumgart, D. C. (2009). The diagnosis and treatment of Crohn’s Disease and ulcerative colitis. Deutsches Arzteblatt International, 106(8), 123–133.Google Scholar
  5. Baumgart, D. C., & Sandborn, W. J. (2012). Crohn’s disease. Lancet, 380(9853), 1590–1605.CrossRefGoogle Scholar
  6. Biancone, L., Calabrese, E., Petruzziello, C., & Pallone, F. (2007). Treatment with biologic therapies and the risk of cancer in patients with IBD. Nature Clinical Practice Gastroenterology & Hepatology, 4(2), 78–91.CrossRefGoogle Scholar
  7. Boyack, K. W., & Klavans, R. (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, 61(12), 2389–2404.CrossRefGoogle Scholar
  8. Cadwallader, J. N. (Editor), Altorjay, I. (Contributor), Ammous, A. (Contributor), Ayadi, S. (Contributor), Bedioui, H. (Contributor). (2008). Crohn’s Disease: Etiology, pathogenesis and interventions (1st ed). Nova Science Publishers.Google Scholar
  9. Calado, P., Cristo, M., Goncalves, M. A., de Moura, E. S., Ribeiro-Neto, B., & Ziviani, N. (2006). Link-based similarity measures for the classification of web documents. Journal of the American Society for Information Science and Technology, 57, 208–221.CrossRefGoogle Scholar
  10. Calado, P., Ribeiro-Neto, B., Ziviani, N., Moura, E., & Silva, I. (2003). Local versus global link information in the web. ACM Transactions on Information Systems, 21, 42–63.CrossRefGoogle Scholar
  11. Cao, M., & Gao, X. (2005). Combining contents and citations for scientific document classification. In AI 2005: Advances in artificial intelligence (pp. 143–152). Berlin: Springer.Google Scholar
  12. Caprilli, R., Angelucci, E., & Clemente, V. (2008). Recent advances in the management of Crohn’s disease. Digestive and Liver Disease, 40(9), 709–716.CrossRefGoogle Scholar
  13. Cheng, Y. X., He, G. R., Mu, X., Zhang, T. T., Li, X. X., Hu, J. J., et al. (2008). Neuroprotective effect of baicalein against MPTP neurotoxicity: Behavioral, biochemical and immunohistochemical profile. Neuroscience Letters, 441(1), 16–20.CrossRefGoogle Scholar
  14. Couto, T., Cristo, M., Gonçalves, M., Calado, P., Ziviani, N., de Moura, E. S., et al. (2006). A comparative study of citations and links in document classification. In Proceedings of the 6th ACM/IEEE-CS joint conference on digital libraries, pp. 75–84.Google Scholar
  15. Dreiseitel, A., Korte, G., Schreier, P., Oehme, A., Locher, S., Domani, M., et al. (2009). Berry anthocyanins and their aglycons inhibit monoamine oxidases A and B. Pharmacological Research, 59(5), 306–311.CrossRefGoogle Scholar
  16. Fink, S. L., & Cookson, B. T. (2005). Apoptosis, pyroptosis, and necrosis: Mechanistic description of dead and dying eukaryotic cells. Infection and Immunity, 73(4), 1907–1916.CrossRefGoogle Scholar
  17. Forte, A., De Sanctis, R., Leonetti, G., Manfredelli, S., Urbano, V., & Bezzi, M. (2008). Dietary chemoprevention of colorectal cancer. Annali Italiani Di Chirurgia, 79(4), 261–267.Google Scholar
  18. Franchi, L., Wamer, N., Viani, K., & Nunez, G. (2009). Function of Nod-like receptors in microbial recognition and host defense. Immunological Reviews, 227, 106–128.CrossRefGoogle Scholar
  19. Glänzel, W., & Czerwon, H. J. (1996). A new methodological approach to bibliographic coupling and its application to the national, regional and institutional level. Scientometrics, 37(2), 195–221.CrossRefGoogle Scholar
  20. Higgins, C. F., & Gottesman, M. M. (1992). Is the multidrug transporter a flippase? Trends Biochemical Science, 17(1), 18–21.CrossRefGoogle Scholar
  21. Huang, S. Q., He, L., Yang, B., & Zhang, M. (2012). A compound correlation model for disjoint literature-based knowledge discovery. Aslib Proceedings, 64(4), 423–436.CrossRefGoogle Scholar
  22. Islam, Z., Hegg, C. C., Bae, H. K., & Pestka, J. J. (2008). Satratoxin G-induced apoptosis in PC-12 neuronal cells is mediated by PKR and caspase independent. Toxicological Sciences, 105(1), 142–152.CrossRefGoogle Scholar
  23. Jacobi, A., Mahler, V., Schuler, G., & Hertl, M. (2006). Treatment of inflammatory dermatoses by tumour necrosis factor antagonists. Journal of the European Academy of Dermatology and Venereology, 20(10), 1171–1187.CrossRefGoogle Scholar
  24. Jankovic, J., & Poewe, W. (2012). Therapies in Parkinson’s disease. Current Opinion in Neurology, 25(4), 433–447.CrossRefGoogle Scholar
  25. Janssens, F. (2007). Clustering of scientific fields by integrating text mining and bibliometrics. Doctoral dissertation. Faculty of Engineering, Katholieke Universiteit Leuven, Belgium.Google Scholar
  26. Janssens, F., Glänzel, W., & De Moor, B. (2008). A hybrid mapping of information science. Scientometrics, 75(3), 607–631.CrossRefGoogle Scholar
  27. Janssens, F., Quoc, V. T., Glänzel, W., & De Moor, B. (2006). Integration of textual content and link information for accurate clustering of science fields. In InSCit2006, Current research in information sciences and technologies: Multidisciplinary approaches to global information systems. I., pp. 615–619.Google Scholar
  28. Janssens, F., Zhang, L., De Moor, B., & Glanzel, W. (2009). Hybrid clustering for validation and improvement of subject-classification schemes. Information Processing & Management, 45(6), 683–702.CrossRefGoogle Scholar
  29. Jarneving, B. (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, 1, 338–356.CrossRefGoogle Scholar
  30. Karypis, G. (2010). CLUTO—A clustering toolkit. http://www.cs.umn.edu/~cluto.
  31. Kessler, M. M. (1963). Bibliographic coupling between scientific papers. American Documentation, 14, 10–25.CrossRefGoogle Scholar
  32. Kostoff, R. N. (2008). Literature-related discovery: Introduction and background. Technological Forecasting and Social Change, 75(2), 165–185.CrossRefGoogle Scholar
  33. Kostoff, R. N. (2010). Literature-related discovery: Common factors for Parkinson’s Disease and Crohn’s Disease. DTIC Technical Report Number ADA525269. (http://www.dtic.mil/) Defense Technical Information Center. Fort Belvoir, VA.
  34. Kostoff, R. N. (2012). Literature-related discovery and innovation—Update. Technological Forecasting and Social Change, 79(4), 789–800.CrossRefGoogle Scholar
  35. Kostoff, R. N., & Briggs, M. B. (2008). Literature-related discovery: Potential treatments for Parkinson’s Disease. Technological Forecasting and Social Change, 75(2), 226–238.CrossRefGoogle Scholar
  36. Kostoff, R. N., Block, J. A., Solka, J. A., Briggs, M. B., Rushenberg, R. L., Stump, J. A., et al. (2009). Literature-related discovery. ARIST, 43, 241–285.Google Scholar
  37. Kostoff, R. N., Briggs, M. B., & Lyons, T. (2008). Literature-related discovery: Potential treatments for multiple sclerosis. Technological Forecasting and Social Change, 75(2), 239–255.CrossRefGoogle Scholar
  38. Lang, A. E. (2007). Parkinsonism. In: L. Goldman & D. Ausiello (eds.), Cecil textbook of medicine (23rd ed, Chap. 433). Philadelphia, PA: Saunders Elsevier.Google Scholar
  39. Lang, A. E. (2009). When and how should treatment be started in Parkinson disease? Neurology, 72(7 Suppl), S39–S43.CrossRefGoogle Scholar
  40. Latella, G., Sferra, R., Vetuschi, A., Zanninelli, G., D’Angelo, A., Catitti, V., et al. (2008). Prevention of colonic fibrosis by Boswellia and Scutellaria extracts in rats with colitis induced by 2,4,5-trinitrobenzene sulphonic acid. European Journal of Clinical Investigation, 38(6), 410–420.CrossRefGoogle Scholar
  41. Layton, D. W., Bogen, K. T., Knize, M. G., Hatch, F. T., Johnson, V. M., & Felton, J. S. (1995). Cancer risk of heterocyclic amines in cooked foods: An analysis and implications for research. Carcinogenesis, 16(1), 39–52.CrossRefGoogle Scholar
  42. Lees, A. J., Hardy, J., & Revesz, T. (2009). Parkinson’s disease. Lancet, 373(9680), 2055–2066.CrossRefGoogle Scholar
  43. Lin, C. C., & Shieh, D. E. (1996). The anti-inflammatory activity of Scutellaria rivularis extracts and its active components, baicalin, baicalein and wogonin. American Center of Chinese Medicine, 24(1), 31–36.MathSciNetCrossRefGoogle Scholar
  44. Liu, X. H., Glanzel, W., & De Moor, B. (2012). Optimal and hierarchical clustering of large-scale hybrid networks for scientific mapping. Scientometrics, 91(2), 473–493.CrossRefGoogle Scholar
  45. Liu, X. H., Yu, S., Janssens, F., Glanzel, W., Moreau, Y., & De Moor, B. (2010). Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database. Journal of the American Society for Information Science and Technology, 61(6), 1105–1119.Google Scholar
  46. Louis, E. D., Jiang, W., Pellegrino, K. M., Rios, E., Factor-Litvak, P., Henchcliffe, C., et al. (2008). Elevated blood harmane (1-methyl-9H-pyrido[3,4-b]indole) concentrations in essential tremor. Neurotoxicology, 29(2), 294–300.CrossRefGoogle Scholar
  47. Maresca, M., Yahi, N., Younes-Sakr, L., Boyron, M., Caporiccio, B., & Fantini, J. (2008). Both direct and indirect effects account for the pro-inflammatory activity of enteropathogenic mycotoxins on the human intestinal epithelium: Stimulation of interleukin-8 secretion, potentiation of interleukin-1 beta effect and increase in the transepithelial passage of commensal bacteria. Toxicology and Applied Pharmacology, 228(1), 84–92.CrossRefGoogle Scholar
  48. Martyn, J. (1964). Bibliographic coupling. Journal of Documentation, 20(4), 236.CrossRefGoogle Scholar
  49. McBrewster, J., Miller, F. P., Vandome, A. F. (Eds.). (2009). Crohn´s Disease: Treatment of Crohns disease, biological therapy for inflammatory bowel disease, Mycobacterium avium subspecies paratuberculosis, Ulcerative colitis, Capsule endoscopy. Alphascript Publishing.Google Scholar
  50. McCoy, M. K., Ruhn, K. A., Martinez, T. N., McAlpine, F. E., Blesch, A., & Tansey, M. G. (2008). Intranigral lentiviral delivery of dominant-negative TNF attenuates neurodegeneration and behavioral deficits in hemiparkinsonian rats. Molecular Therapy, 16(9), 1572–1579.CrossRefGoogle Scholar
  51. Miller, C. M., Rindflesch, T. C., Fiszman, M., Hristovski, D., Shin, D., Rosemblat, G., et al. (2012). A closed literature-based discovery technique finds a mechanistic link between hypogonadism and diminished sleep quality in aging men. Sleep, 35(2), 279–285.Google Scholar
  52. Olanow, C. W., Stern, M. B., & Sethi, K. (2009). The scientific and clinical basis for the treatment of Parkinson disease. Neurology, 72(21 Suppl 4), S1–S136.CrossRefGoogle Scholar
  53. Osman, N., Adawi, D., Ahrne, S., Jeppsson, B., & Molin, G. (2008). Probiotics and blueberry attenuate the severity of dextran sulfate sodium (DSS)-induced colitis. Digestive Diseases and Sciences, 53(9), 2464–2473.CrossRefGoogle Scholar
  54. Pahnke, J., Walker, L. C., Scheffler, K., & Krohn, M. (2009). Alzheimer’s disease and blood-brain barrier function—Why have anti-beta-amyloid therapies failed to prevent dementia progression? Neuroscience and Biobehavioral Reviews, 33(7), 1099–1108.CrossRefGoogle Scholar
  55. Panes, J., Gomollon, F., Taxonera, C., Hinojosa, J., Clofent, J., & Nos, P. (2007). Crohn’s disease—A review of current treatment with a focus on biologics. Drugs, 67(17), 2511–2537.CrossRefGoogle Scholar
  56. Paris, I., Perez-Pastene, C., Couve, E., Caviedes, P., LeDoux, S., & Segura-Aguilar, J. (2009). Copper dopamine complex induces mitochondrial autophagy preceding caspase-independent apoptotic cell death. Journal of Biological Chemistry, 284(20), 13306–13315.CrossRefGoogle Scholar
  57. Pestka, J. J., Zhou, H. R., Moon, Y., & Chung, Y. J. (2004). Cellular and molecular mechanisms for immune modulation by deoxynivalenol and other trichothecenes: Unraveling a paradox. Toxicology Letters, 153(1), 61–73.CrossRefGoogle Scholar
  58. Pinton, P., Nougayrede, J. P., Del Rio, J. C., Moreno, C., Marin, D. E., Ferrier, L., et al. (2009). The food contaminant deoxynivalenol, decreases intestinal barrier permeability and reduces claudin expression. Toxicology and Applied Pharmacology, 237(1), 41–48.CrossRefGoogle Scholar
  59. Poewe, W. (2009). Treatments for Parkinson disease—Past achievements and current clinical needs. Neurology, 72(7 Suppl), S65–S73.CrossRefGoogle Scholar
  60. Sarkar, I. N. (2012). A vector space model approach to identify genetically related diseases. Journal of the American Medical Informatics Association, 19(2), 249–254.CrossRefGoogle Scholar
  61. Schapira, A. H., Agid, Y., Barone, P., Jenner, P., Lemke, M. R., Poewe, W., et al. (2009). Perspectives on recent advances in the understanding and treatment of Parkinson’s disease. European Journal of Neurology, 16(10), 1090–1099.CrossRefGoogle Scholar
  62. Scheinfeld, N. (2004). A comprehensive review and evaluation of the side effects of the tumor necrosis factor alpha blockers etanercept, infliximab and adalimumab. Journal of Dermatological Treatment, 15(5), 280–294.CrossRefGoogle Scholar
  63. Schiminovich, S. (1971). Automatic classification and retrieval of documents by means of a bibliographic pattern discovery algorithm. Information Storage and Retrieval, 6, 417–435.CrossRefGoogle Scholar
  64. Search (2010). Search Technology, Inc., 6025 The Corners Parkway, Suite 202, Norcross, GA 30092, http://www.thevantagepoint.com. 2010.
  65. Sen, S. K., & Gan, S. K. (1983). A mathematical extension of the idea of bibliographic coupling and its applications. Annals of Library Science and Documentation, 30(2), 78–82.Google Scholar
  66. Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2009). Comparative study on methods of detecting research fronts using different types of citation. Journal of the American Society for Information Science and Technology, 60(3), 571–580.CrossRefGoogle Scholar
  67. Shtilbans, A., & Henchcliffe, C. (2012). Biomarkers in Parkinson’s disease: An update. Current Opinion in Neurology, 25(4), 460–465.CrossRefGoogle Scholar
  68. Smalheiser, N. R. (2005). The arrowsmith project: 2005 status report. Discovery Science, Proceedings Book Series: Lecture Notes in Computer Science, 3735, 26–43.Google Scholar
  69. Smalheiser, N. R., & Swanson, D. R. (1996). Linking estrogen to Alzheimer’s Disease: An informatics approach. Neurology, 47, 809–810.CrossRefGoogle Scholar
  70. Srinivasan, P. (2004). Text mining: Generating hypotheses from MEDLINE. JASIST, 55(5), 396–413.CrossRefGoogle Scholar
  71. Swanson, D. R. (1987). Two medical literatures that are logically but not bibliographically connected. Journal of the American Society for Information Science, 38(4), 228–233.Google Scholar
  72. Swanson, D. R. (1988). Migraine and magnesium: Eleven neglected connections. Perspectives in Biology and Medicine, 31, 526–557.Google Scholar
  73. Swanson, D. R. (1990). Somatomedin C and arginine; implicit connections between mutually-isolated literatures. Perspectives in Biology and Medicine, 33, 157–186.Google Scholar
  74. Swanson, D. R. (2008). Running, esophageal acid reflux, and atrial fibrillation: A chain of events linked by evidence from separate medical literatures. Medical Hypotheses, 71(2), 178–185.CrossRefGoogle Scholar
  75. Swanson, D. R., Smalheiser, N. R., & Bookstein, A. (2001). Information discovery from complementary literatures: Categorizing viruses as potential weapons. JASIST, 52, 797–812.CrossRefGoogle Scholar
  76. Torvik, V. I., & Smalheiser, N. R. (2007). A quantitative model for linking two disparate sets of articles in Medline. Bioinformatics, 23(13), 1658–1665.CrossRefGoogle Scholar
  77. Wang, J., & Mazza, G. (2002). Effects of anthocyanins and other phenolic compounds on the production of tumor necrosis factor alpha in LPS/IFN-gamma-activated RAW 264.7 macrophages. Journal of Agricultural and Food Chemistry, 50(15), 4183–4189.CrossRefGoogle Scholar
  78. Wilkins, T., Jarvis, K., & Patel, J. (2011). Diagnosis and management of Crohn’s disease. American Family Physician, 84(12), 1365–1375.Google Scholar
  79. Wren, J. D., Bekeredjian, R., Stewart, J. A., Shohet, R. V., & Garner, H. R. (2004). Knowledge discovery by automated identification and ranking of implicit relationships. Bioinformatics, 20(3), 389–398.CrossRefGoogle Scholar
  80. Zhu, S., Yu, K., Chi, Y., & Gong, Y. (2007). Combining content and link for classification using matrix factorization. In Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, pp. 487–494.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2014

Authors and Affiliations

  1. 1.The MITRE Corporation BedfordUSA
  2. 2.School of Public PolicyGeorgia Institute of TechnologyAtlanta USA

Personalised recommendations