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

doi:10.1007/978-3-030-04167-0_52

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
Editors	Long Cheng, Andrew Chi Sing Leung, Seiichi Ozawa
Publisher	Springer Verlag
Pages	571-581
Number of pages	11
ISBN (Print)	9783030041663
DOIs	https://doi.org/10.1007/978-3-030-04167-0_52
Publication status	Published - 2018
Event	25th International Conference on Neural Information Processing, ICONIP 2018 - Siem Reap, Cambodia Duration: 13 Dec 2018 → 16 Dec 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11301 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	25th International Conference on Neural Information Processing, ICONIP 2018
Country/Territory	Cambodia
City	Siem Reap
Period	13/12/18 → 16/12/18

Keywords

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

Access to Document

10.1007/978-3-030-04167-0_52

Cite this

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 (pp. 571-581). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11301 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-04167-0_52

Nandini, Durgesh ; Capecci, Elisa ; Koefoed, Lucien et al. / Modelling and analysis of temporal gene expression data using spiking neural networks. Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. editor / Long Cheng ; Andrew Chi Sing Leung ; Seiichi Ozawa. Springer Verlag, 2018. pp. 571-581 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{16001bba7d33424cb52a3db41d0a3aa9,

title = "Modelling and analysis of temporal gene expression data using spiking neural networks",

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 = "Gene expression, Gene interaction networks, Microarray, Spiking neural networks, Transcriptome data analysis",

author = "Durgesh Nandini and Elisa Capecci and Lucien Koefoed and Ibai La{\~n}a and Shahi, {Gautam Kishore} and Nikola Kasabov",

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year = "2018",

doi = "10.1007/978-3-030-04167-0_52",

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Nandini, D, Capecci, E, Koefoed, L, Laña, I, Shahi, GK & Kasabov, N 2018, Modelling and analysis of temporal gene expression data using spiking neural networks. in L Cheng, ACS 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

Modelling and analysis of temporal gene expression data using spiking neural networks. / Nandini, Durgesh; Capecci, Elisa; Koefoed, Lucien et al.
Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. ed. / Long Cheng; Andrew Chi Sing Leung; Seiichi Ozawa. Springer Verlag, 2018. p. 571-581 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11301 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Modelling and analysis of temporal gene expression data using spiking neural networks

AU - Nandini, Durgesh

AU - Capecci, Elisa

AU - Koefoed, Lucien

AU - Laña, Ibai

AU - Shahi, Gautam Kishore

AU - Kasabov, Nikola

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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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KW - Gene interaction networks

KW - Microarray

KW - Spiking neural networks

KW - Transcriptome data analysis

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings

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A2 - Leung, Andrew Chi Sing

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PB - Springer Verlag

T2 - 25th International Conference on Neural Information Processing, ICONIP 2018

Y2 - 13 December 2018 through 16 December 2018

ER -

Nandini D, Capecci E, Koefoed L, Laña I, Shahi GK, Kasabov N. Modelling and analysis of temporal gene expression data using spiking neural networks. In Cheng L, Leung ACS, Ozawa S, editors, Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings. Springer Verlag. 2018. p. 571-581. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-04167-0_52

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

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