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Probabilistic learning in immune network: Weighted tree matching model

DSpace at IIT Bombay

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Title Probabilistic learning in immune network: Weighted tree matching model
 
Creator JOSHI, RR
KRISHNANAND, K
 
Subject antigen-antibody recognition
system
 
Description 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.
 
Publisher MARY ANN LIEBERT INC PUBL
 
Date 2011-08-18T14:22:59Z
2011-12-26T12:55:47Z
2011-12-27T05:42:32Z
2011-08-18T14:22:59Z
2011-12-26T12:55:47Z
2011-12-27T05:42:32Z
1996
 
Type Article
 
Identifier JOURNAL OF COMPUTATIONAL BIOLOGY, 3(1), 143-162
1066-5277
http://dx.doi.org/10.1089/cmb.1996.3.143
http://dspace.library.iitb.ac.in/xmlui/handle/10054/10045
http://hdl.handle.net/10054/10045
 
Language en