Integration of Instance-Based Learning and Text Mining for Identification of Potential Virus/Bacterium as Bio-terrorism Weapons
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.
KeywordsMeSH Term Encephalitis Virus Biomedical Literature Rift Valley Fever Rift Valley Fever Virus
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