Abstract:
We propose a latent feature model for immunosignature random peptide microarray data using beta process factor analysis to identify relationships between patients and inf...Show MoreMetadata
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.
Published in: 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)
Date of Conference: 04-07 November 2012
Date Added to IEEE Xplore: 28 March 2013
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