Abstract
There are some viruses and bacteria that have been identified as bioterrorism weapons. However, there are a lot other viruses and bacteria that can be potential bioterrorism weapons. A system that can automatically suggest potential bioterrorism weapons will help laypeople to discover these suspicious viruses and bacteria. In this paper we apply instance-based learning & text mining approach to identify candidate viruses and bacteria as potential bio-terrorism weapons from biomedical literature. We first take text mining approach to identify topical terms of existed viruses (bacteria) from PubMed separately. Then, we use the term lists as instances to build matrices with the remaining viruses (bacteria) to discover how much the term lists describe the remaining viruses (bacteria). Next, we build a algorithm to rank all remaining viruses (bacteria). We suspect that the higher the ranking of the virus (bacterium) is, the more suspicious they will be potential bio-terrorism weapon. Our findings are intended as a guide to the virus and bacterium literature to support further studies that might then lead to appropriate defense and public health measures.
This work is supported partially by the NSF Career grant IIS 0448023 and NSF 0514679 and PA Dept of Health Tobacco Formula Grants.
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References
SdfsdfBüchen-Osmond, C.: Taxonomy and Classification of Viruses. In: Manual of Clinical Microbiology, 8th edn., vol. 2, pp. 1217–1226. ASM Press, Washington (2003)
DiGiacome, R.A., Kremer, J.M., Shah, D.M.: Fish oil dietary supplementation is patients with Raynaud’s phenomenon: a double-blind, controlled, prospective study. American Journal of Medicine 8, 158–164 (1989)
Geissler, E. (ed.): Biological and toxin weapons today. SIPRI, Oxford (1986)
Swanson, D.R., Smalheiser, N.R., Bookstein, A.: Information discovery from complementary literatures: categorizing viruses as potential weapons. JASIST 52(10), 797–812 (2001)
Swanson, D.R.: Fish-oil, Raynaud’s Syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine 30(1), 7–18 (1986)
Swanson, D.R.: Undiscovered public knowledge. Libr. Q. 56(2), 103–118 (1986)
Hu, X., Yoo, I., Rumm, P., Atwood, M.E.: Mining candidate viruses as potential bio-terrorism weapons from biomedical literature. In: Kantor, P., Muresan, G., Roberts, F., Zeng, D.D., Wang, F.-Y., Chen, H., Merkle, R.C. (eds.) ISI 2005. LNCS, vol. 3495, pp. 60–71. Springer, Heidelberg (2005)
Kankar, P., Adak, S., Sarkar, A., Murari, K.K., Sharma, G.: MedMeSH Summarizer: Text Mining for Gene Clusters. In: The Proceedings of the Second SIAM International Conference on Data Mining, Arlington, VA (2002)
Guidance on cooperative agreements from the U.S. Department of Health and Human Services, Centers for Disease Control and Prevention and the Human Resource Service Administration. Accessible at, http://www.bt.cdc.gov
Rumm, P.D.: Bioterrorism preparedness: potential threats remain. Am. J. Public. Health 95(3), 372 (2005) (comment on previous article)
Rumm, P., Gaydos, J., Mansfield, J., Kelley, P.: A Department of Defense (DOD) Virtual Public Health Laboratory Directory. In: Mil. Med., vol. 165(Supp. 2), p. 73 (July 2000)
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Hu, X., Zhang, X., Wu, D., Zhou, X., Rumm, P. (2006). Integration of Instance-Based Learning and Text Mining for Identification of Potential Virus/Bacterium as Bio-terrorism Weapons. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_55
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DOI: https://doi.org/10.1007/11760146_55
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