Abstract

As recently discovered, a comprehensive profiling of antibodies in a patient's blood can be obtained using random-sequence peptides on microarrays and analyzed for medical diagnosis. In this paper, we propose a novel adaptive learning methodology for biothreat detection and classification, which extracts and models appropriate stochastic features from such immunosignatures. The technique is based on the use of Dirichlet process mixture models to adaptively cluster the microarray measurements in feature space. This learning-while- classifying strategy provides the capability of adaptively detecting new biothreat agents on the fly. We demonstrate the utility of the proposed method by classifying diseases using real experimental peptide microarray data.

Original languageEnglish (US)
Title of host publicationConference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
Pages1883-1887
Number of pages5
DOIs
StatePublished - Dec 1 2011
Event45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 - Pacific Grove, CA, United States
Duration: Nov 6 2011Nov 9 2011

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Other

Other45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011
CountryUnited States
CityPacific Grove, CA
Period11/6/1111/9/11

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ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Malin, A., Zhang, J. J., Chakraborty, B., Kovvali, N., Papandreou-Suppappola, A., Johnston, S., & Stafford, P. (2011). Adaptive learning of immunosignaturing peptide array features for biothreat detection and classification. In Conference Record of the 45th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2011 (pp. 1883-1887). [6190350] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2011.6190350