Predicting emerging technologies with the aid of text-based data mining: the micro approach

Text data mining should be useful for anticipating new technologies and new uses for existing technologies, insofar as one can attempt to connect complementary pieces of information across two different domains, or subsets, of the scientific literature. The present study attempted to predict genetic engineering technologies that may impact on viral warfare in the future. The analysis was carried out using a combination of conventional Medline searches and the package of advanced informatics techniques known collectively as Arrowsmith. The findings strongly indicate that genetic packaging technologies such as DEAE-dextran, cationic liposomes and cyclodextrins are plausible candidates to enhance infections caused by viruses delivered via an aerosol route despite the fact that no studies have yet been reported that have examined this issue directly, and certainly not in the contexts of viral disease or viral warfare. The critical factor was the overall strategy of approaching the problem: first, to define two specific fields explicitly (in this case, genetic engineering and viral warfare) that are hypothesized to contain complementary information; second, to identify common factors that bridge the two disciplines (i.e. research on viruses); and third, to progressively shape the query once initial findings are obtained. Thus, in contrast to some current perceptions, the process of text data mining is neither automatic nor is it restricted to those who have access to macro analyses using customized computer systems. (C) 2001 Elsevier Science Ltd. All rights reserved.