Modelling and analysis of temporal gene expression data using spiking neural networks

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URI: https://hdl.handle.net/11556/2213
ISSN: 0302-9743
ISBN: 9783030041663
DOI: 10.1007/978-3-030-04167-0_52
Publication date
2018
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Springer Verlag
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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.
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Publisher Copyright: © 2018, Springer Nature Switzerland AG.
Keywords
Gene expression , Gene interaction networks , Microarray , Spiking neural networks , Transcriptome data analysis , Theoretical Computer Science , General Computer Science
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Nandini , D , Capecci , E , Koefoed , L , Laña , I , Shahi , G K & Kasabov , N 2018 , Modelling and analysis of temporal gene expression data using spiking neural networks . in L Cheng , A C S Leung & S Ozawa (eds) , Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings . Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 11301 LNCS , Springer Verlag , pp. 571-581 , 25th International Conference on Neural Information Processing, ICONIP 2018 , Siem Reap , Cambodia , 13/12/18 . https://doi.org/10.1007/978-3-030-04167-0_52
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