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CNGM as Integration of GPRN, ANN and Evolving Processes

  • Lubica Benuskova
  • Nikola Kasabov
Chapter
  • 562 Downloads
Part of the Topics in Biomedical Engineering. International Book Series book series (ITBE)

Abstract

This chapter presents a methodology for CNGM that integrates gene regulatory networks with models of artificial neural networks to model different functions of neural system. Properties of all cell types, including neurons, are determined by proteins they contain (Lodish et al. 2000). In tum, the types and amounts of proteins are determined by differential transcription of different genes in response to internal and external signals. Eventually, the properties of neurons determine the structure and dynamics of the whole neural network they are part of. Interaction of genes in neurons affects the dynamics of the whole neural network model through neuronal parameters, which are no longer constant, but change as a function of gene expression. Through optimization of the gene interaction network, initial gene/protein expression values and neuronal parameters, particular target states of the neural network operation can be achieved, and meaningful relationships between genes, proteins and neural functions can be extracted.

Keywords

Artificial Neural Network Gene Regulatory Network Gene Interaction Gene Interaction Network Artificial Neural Network Output 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science + Business Media, LLC 2007

Authors and Affiliations

  • Lubica Benuskova
    • 1
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAUTAucklandNew Zealand

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