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Modelling gene interaction networks from time-series gene expression data using evolving spiking neural networks

  • Elisa CapecciEmail author
  • Jesus L. Lobo
  • Ibai Laña
  • Josafath I. Espinosa-Ramos
  • Nikola Kasabov
Original Paper

Abstract

The genetic mechanisms responsible for the differentiation, metabolism, morphology and function of a cell in both normal and abnormal conditions can be uncovered by the analysis of transcriptomes. Mining big data such as the information encoded in nucleic acids, proteins, and metabolites has challenged researchers for several years now. Even though bioinformatics and system biology techniques have improved greatly and many improvements have been done in these fields of research, most of the processes that influence gene interaction over time are still unknown. In this study, we apply state-of-the art spiking neural network techniques to model, analyse and extract information about the regulatory processes of gene expression over time. A case study of microarray profiling in human skin during elicitation of eczema is used to examine the temporal association of genes involved in the inflammatory response, by means of a gene interaction network. Spiking neural network techniques are able to learn the interaction between genes using information encoded from the time-series gene expression data as spikes. The temporal interaction is learned, and the patterns of activity extracted and analysed with a gene interaction network. Results demonstrated that useful knowledge can be extracted from the data by using spiking neural network, unlocking some of the possible mechanisms involved in the regulatory process of gene expression.

Keywords

Artificial intelligence Evolving spiking neural networks Transcriptome Gene expression Microarray Data analysis Gene interaction networks Nickel allergy Allergic contact dermatitis 

Notes

Acknowledgements

Several people have contributed to the research that resulted in this work, especially: Dr. Y. Chen, Dr. J. Hu, L. Zhou, Dr. E. Tu and Maryam Gholami-Doborjeh. Many thanks to Caitlin Veale and Kate Steckmest for proofreading the manuscript. Funding was provided by Auckland University of Technology, New Zealand (SRIF INTERACT 2017-18).

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Knowledge Engineering and Discovery Research Institute of the Auckland University of TechnologyAucklandNew Zealand
  2. 2.Tecnalia Research and InnovationDerioSpain

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