Probabilistic learning in immune network: weighted tree matching model.

R R Joshi; K Krishnanand

doi:10.1089/cmb.1996.3.143

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PubMed · 8697233

Probabilistic learning in immune network: weighted tree matching model.

R R Joshi·K Krishnanand

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Abstract

Adaptive learning properties (of clonal selection and affinity maturation) in the immune network model are investigated in this paper under a nonlinear data structural representation of the involved molecules. Weighted trees are constructed to model the multiple paratopes/epitopes on the antibodies/antigens. Parallel computing experiments are carried out for the canonical coding of these trees and the corresponding multiple matching interactions. Our experiments on real data have shown significant results on the cognitive properties of the immune network. These and other computational results are presented along with a discussion of future applications.

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Amino Acid Sequence Antibodies Antigens Computer Simulation Epitopes Immune System Immunologic Memory Models, Immunological Molecular Sequence Data Probability Journal Article Research Support, Non-U.S. Gov't

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R R Joshi, K Krishnanand. 1996. Probabilistic learning in immune network: weighted tree matching model.. https://doi.org/10.1089/cmb.1996.3.143

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