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Proceedings Paper

Biological agent detection and identification using pattern recognition
Author(s): Jerome J. Braun; Yan Glina; Nicholas Judson; Kevin D. Transue
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Paper Abstract

This paper discusses a novel approach for the automatic identification of biological agents. The essence of the approach is a combination of gene expression, microarray-based sensing, information fusion, machine learning and pattern recognition. Integration of these elements is a distinguishing aspect of the approach, leading to a number of significant advantages. Amongst them are the applicability to various agent types including bacteria, viruses, toxins, and other, ability to operate without the knowledge of a pathogen's genome sequence and without the need for bioagent-speciific materials or reagents, and a high level of extensibility. Furthermore, the approach allows detection of uncatalogued agents, including emerging pathogens. The approach offers a promising avenue for automatic identification of biological agents for applications such as medical diagnostics, bioforensics, and biodefense.

Paper Details

Date Published: 12 May 2005
PDF: 12 pages
Proc. SPIE 5795, Chemical and Biological Sensing VI, (12 May 2005); doi: 10.1117/12.605913
Show Author Affiliations
Jerome J. Braun, MIT Lincoln Lab. (United States)
Yan Glina, MIT Lincoln Lab. (United States)
Nicholas Judson, MIT Lincoln Lab. (United States)
Kevin D. Transue, MIT Lincoln Lab. (United States)


Published in SPIE Proceedings Vol. 5795:
Chemical and Biological Sensing VI
Patrick J. Gardner, Editor(s)

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