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
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
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.
KW - Gene expression
KW - Gene interaction networks
KW - Microarray
KW - Spiking neural networks
KW - Transcriptome data analysis
UR - https://www.scopus.com/pages/publications/85059050546
U2 - 10.1007/978-3-030-04167-0_52
DO - 10.1007/978-3-030-04167-0_52
M3 - Conference contribution
AN - SCOPUS:85059050546
SN - 9783030041663
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 571
EP - 581
BT - Neural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
A2 - Cheng, Long
A2 - Leung, Andrew Chi Sing
A2 - Ozawa, Seiichi
PB - Springer Verlag
T2 - 25th International Conference on Neural Information Processing, ICONIP 2018
Y2 - 13 December 2018 through 16 December 2018
ER -