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Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding

Brusic, Vladimir, Bucci, Kim, Schonbach, Christian, Petrovsky, Nikolai, Zeleznikow, John and Kazura, James W (2001) Efficient discovery of immune response targets by cyclical refinement of QSAR models of peptide binding. Journal of Molecular Graphics and Modelling, 19 (5). pp. 405-411. ISSN 1093-3263

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Laboratory experimentation in immunology, particularly that related to antigen recognition and determination of specific targets of immune response, has become a combinatorial challenge. Understanding the mechanisms of immune recognition and specificity and selection of immune response targets is necessary before this information can be applied systematically to the design of vaccines and immunotherapeutics. We anticipate that dynamic models of immune interactions that can absorb the ever-increasing amount of data generated in the field and self-improve with the accumulation of data and knowledge will become standard methodology in immunology research. Standardization and exploitation of the synergies of modelling and experimental methods provide an efficient means for largescale epitope screening. This study provides a first-level guideline for cyclical refinement of computer models and their integration with laboratory experiments.

Item Type: Article
Uncontrolled Keywords: ResPubID12223, T-cell epitopes, malaria vaccine, T lymphocytes, major histocompatibility complex, MHC molecules, artificial neural networks, ANNs, hidden Markov models, HMMs
Subjects: Historical > RFCD Classification > 280000 Information, Computing and Communication Sciences
Current > FOR Classification > 0601 Biochemistry and Cell Biology
Current > FOR Classification > 0801 Artificial Intelligence and Image Processing
Current > FOR Classification > 1107 Immunology
Historical > Faculty/School/Research Centre/Department > School of Management and Information Systems
Depositing User: VUIR
Date Deposited: 27 Aug 2013 00:14
Last Modified: 23 Aug 2020 23:01
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Citations in Scopus: 36 - View on Scopus

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