Abstract

Previously, adaptive learning algorithms have been used with immunosignaturing in order to identify disease states in patients. However, in these algorithms the presence of a single disease state is assumed, although in a clinical setting this may not be the case. We propose a novel algorithm based on latent feature identification using beta process factor analysis, in which the binary feature sharing matrix is modified and key comparisons are applied to identify multiple possible underlying disease states. The algorithm is verified using combinations of actual patient immunosignaturing data. The proposed method has a variety of applications, including multi-disease state diagnosis in the clinical setting, multi-biothreat detection in the field, and separation of co-contaminated biological samples.

Original languageEnglish (US)
Title of host publicationConference Record of the 47th Asilomar Conference on Signals, Systems and Computers
PublisherIEEE Computer Society
Pages1301-1305
Number of pages5
ISBN (Print)9781479923908
DOIs
StatePublished - Jan 1 2013
Event2013 47th Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duration: Nov 3 2013Nov 6 2013

Publication series

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

Other

Other2013 47th Asilomar Conference on Signals, Systems and Computers
CountryUnited States
CityPacific Grove, CA
Period11/3/1311/6/13

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ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

Cite this

Malin, A., Kovvali, N., Papandreou-Suppappola, A., O'Donnell, B., Johnston, S., & Stafford, P. (2013). Adaptive learning of immunosignaturing features for multi-disease pathologies. In Conference Record of the 47th Asilomar Conference on Signals, Systems and Computers (pp. 1301-1305). [6810504] (Conference Record - Asilomar Conference on Signals, Systems and Computers). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2013.6810504