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Modelling and Analysis of Temporal Gene Expression Data Using Spiking Neural Networks

  • Durgesh Nandini
  • Elisa Capecci
  • Lucien Koefoed
  • Ibai Laña
  • Gautam Kishore Shahi
  • Nikola Kasabov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11301)

Abstract

Analysis of temporal gene expression data poses a significant challenge due to the combination of high dimensionality and low sample size. The purpose of this paper is to present a methodology for classification, modelling, and analysis of short time-series gene expression data using spiking neural networks (SNN) and to uncover temporal expression patterns for knowledge discovery. The classification is based on the NeuCube SNN model. Time-series gene expression data of mouse primary cortical neurons is examined as a case study. The results of the analysis are promising, indicating that SNN methodologies can be effectively used to model and analyse temporal gene expression data with surpassing performance over traditional machine learning algorithms. Additionally, a gene interaction network is constructed from the temporal gene activity modelled using the NeuCube architecture offering a new way of knowledge discovery. Future work will be directed towards using gene interactions networks to help guide pharmacological research for dementia.

Keywords

Spiking neural networks Gene interaction networks Gene expression Microarray Transcriptome data analysis 

Notes

Acknowledgment

The SRIF 2017–2018 INTERACT project of the Auckland University of Technology supports the presented study. Several people have contributed to the research that resulted in this paper, especially: Dr Y.Chen, Dr J.Hu, L.Zhou, Dr E. Tu and Maryam Gholami-Doborjeh. A free for research and teaching version of the NeuCube SNN system can be found from the KEDRI web site: https://kedri.aut.ac.nz/R-and-D-Systems/neucube.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Durgesh Nandini
    • 1
  • Elisa Capecci
    • 2
  • Lucien Koefoed
    • 2
  • Ibai Laña
    • 3
  • Gautam Kishore Shahi
    • 1
  • Nikola Kasabov
    • 2
  1. 1.Dipartimento di Ingegneria e Scienza dell’Informazione (DISI), University of TrentoPovo, TrentoItaly
  2. 2.Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland University of Technology (AUT)AucklandNew Zealand
  3. 3.OPTIMA Unit. TECNALIA. P. Tecnologico Bizkaia, Ed. 700DerioSpain

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