A self-organizing cognitive network of antibody repertoire development
DSpace at IIT Bombay
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Title |
A self-organizing cognitive network of antibody repertoire development
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Creator |
JOSHI, RR
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Subject |
3-dimensional structure
crystal-structure antigen complex immune-system recognition antibody shape-space cognitive networks immune network model weight-vectors for probabilistic learning |
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Description |
A self-organizing cognitive network is mapped here onto the Id network model. The weight-vectors in this network represent some important topographical and biophysical parameters in the antibody-antigen affinity landscape, The Kohonen layers in the network correspond to affinity clones and the involved algorithm simulates the operations of clonal selection, hypermutation, differentiation, diversity, and affinity maturation. Two significant features of this model are: (i) a computationally feasible and biophysically informative representation of the para/epitopes, and (ii) the ability to perform simultaneous (parallel) and associative computations in a multidimensional shape-space, Computational experiments with real data have shown cognitive properties of this network, The results also indicate scope in quantitative characterization of the metadynamics of the above operations/weights in the adaptive development of the antibody repertoire.
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Publisher |
MARY ANN LIEBERT INC PUBL
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Date |
2011-08-18T14:11:45Z
2011-12-26T12:55:47Z 2011-12-27T05:42:31Z 2011-08-18T14:11:45Z 2011-12-26T12:55:47Z 2011-12-27T05:42:31Z 1996 |
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Type |
Article
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Identifier |
JOURNAL OF COMPUTATIONAL BIOLOGY, 3(4), 529-545
1066-5277 http://dx.doi.org/10.1089/cmb.1996.3.529 http://dspace.library.iitb.ac.in/xmlui/handle/10054/10043 http://hdl.handle.net/10054/10043 |
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Language |
en
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