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Adaptive Learning and Unsupervised Clustering of Immune Responses Using Microarray Random Sequence Peptides

Abstract Immunosignaturing is a medical test for assessing the health status of a patient by applying microarrays of random sequence peptides to determine the patient's immune fingerprint by associating antibodies from a biological sample to immune responses. The immunosignature measurements can potentially provide pre-symptomatic diagnosis for infectious diseases or detection of biological threats. Currently, traditional bioinformatics tools, such as data mining classification algorithms, are used to process the large amount of peptide microarray data. However, these methods generally require training data and do not adapt to changing immune conditions or additional patient information. This work proposes advanced processing techniques to impro... (more)
Created Date 2013
Contributor Malin, Anna (Author) / Papandreou-Suppappola, Antonia (Advisor) / Bliss, Daniel (Committee member) / Chakrabarti, Chaitali (Committee member) / Kovvali, Narayan (Committee member) / Lacroix, Zoe (Committee member) / Arizona State University (Publisher)
Subject Electrical engineering
Type Doctoral Dissertation
Extent 159 pages
Language English
Reuse Permissions All Rights Reserved
Note Ph.D. Electrical Engineering 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis