Neural networks and temporal gene expression data   [NN] [GE]

by

Krishna, A., Narayanan, A. and Keedwell, E., C.

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Info: Applications of Evolutionary Computing, EvoWorkshops2005: EvoBIO, EvoCOMNET, EvoHOT, EvoIASP, EvoMUSART, EvoSTOC (Conference proceedings), 2005, p. 61-70
Keywords:evolutionary computation
Abstract:
Temporal gene expression data [GE] is of particular interest to systems biology researchers. Such data can be used to create gene networks, where such networks represent the regulatory interactions between genes over time. Reverse engineering gene networks from temporal gene expression data [RE] [GE] is one of the most important steps in the study of complex biological systems. This paper introduces sensitivity analysis of systematically perturbed trained neural networks to [NN] both select a smaller and more influential subset of genes from a temporal gene expression [GE] dataset as well as reverse engineer a gene network from the reduced temporal gene expression data. [GE] The methodology was applied to the rat cervical spinal cord development time-course data, and it is demonstrated that the method not only identifies important genes involved in regulatory relationships but also generates candidate gene networks for further experimental study.
Notes:
EvoWorkshops2005

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BibTex:
@InProceedings{krishna:evows05,
  author = {Abhay Krishna and Ajit Narayanan and E. C. Keedwell},
  title = {Neural networks and temporal gene expression data},
  booktitle = {Applications of Evolutionary Computing, EvoWorkshops2005: {EvoBIO}, {EvoCOMNET}, {EvoHOT}, {EvoIASP}, {EvoMUSART}, {EvoSTOC}},
  year = {2005},
  month = {30 March-1 April},
  editor = {Franz Rothlauf and Juergen Branke and Stefano Cagnoni and David W. Corne and Rolf Drechsler and Yaochu Jin and Penousal Machado and Elena Marchiori and Juan Romero and George D. Smith and Giovanni Squillero},
  series = {LNCS},
  volume = {3449},
  publisher = {Springer Verlag},
  address = {Lausanne, Switzerland},
  publisher_address = {Berlin},
  pages = {61--70},
  keywords =    {evolutionary computation},
  ISBN =        {},
  ISSN =        {0302-9743},
  url =         {},
  size =        {},
  abstract =    {Temporal gene expression data is of particular
  interest to systems biology researchers. Such data can be used to
  create gene networks, where such networks represent the regulatory
  interactions between genes over time. Reverse engineering gene
  networks from temporal gene expression data is one of the most
  important steps in the study of complex biological systems. This
  paper introduces sensitivity analysis of systematically perturbed
  trained neural networks to both select a smaller and more
  influential subset of genes from a temporal gene expression dataset
  as well as reverse engineer a gene network from the reduced temporal
  gene expression data. The methodology was applied to the rat
  cervical spinal cord development time-course data, and it is
  demonstrated that the method not only identifies important genes
  involved in regulatory relationships but also generates candidate
  gene networks for further experimental study.}, 
  notes =       {EvoWorkshops2005}
}