Abstract

We propose a latent feature model for immunosignature random peptide microarray data using beta process factor analysis to identify relationships between patients and infectious agents. The method uses Bayesian nonparametric adaptive learning techniques that allow for further classification if additional patient data is received, and new relationships between patients and disease states are obtained. In addition to feature discovery, this methodology can also detect biothreat agents on the fly. Using experimental immunosignature microarray data, we demonstrate the identification and classification of underlying relationships between patients with different disease states.

Original languageEnglish (US)
Title of host publicationConference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
Pages1651-1655
Number of pages5
DOIs
StatePublished - Dec 1 2012
Event46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 - Pacific Grove, CA, United States
Duration: Nov 4 2012Nov 7 2012

Publication series

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

Other

Other46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012
CountryUnited States
CityPacific Grove, CA
Period11/4/1211/7/12

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Malin, A., Kovvali, N., Papandreou-Suppappola, A., Zhang, J. J., Johnston, S., & Stafford, P. (2012). Beta process based adaptive learning for immunosignature microarray feature identification. In Conference Record of the 46th Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2012 (pp. 1651-1655). [6489312] (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2012.6489312