RT Conference Proceedings
T1 Modelling and analysis of temporal gene expression data using spiking neural networks
A1 Nandini, Durgesh
A1 Capecci, Elisa
A1 Koefoed, Lucien
A1 Laña, Ibai
A1 Shahi, Gautam Kishore
A1 Kasabov, Nikola
A2 Cheng, Long
A2 Leung, Andrew Chi Sing
A2 Ozawa, Seiichi
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.
PB Springer Verlag
SN 9783030041663
SN 0302-9743
YR 2018
FD 2018
LK https://hdl.handle.net/11556/2213
UL https://hdl.handle.net/11556/2213
LA eng
NO 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
NO conference
NO Publisher Copyright: © 2018, Springer Nature Switzerland AG.
NO 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.
DS TECNALIA Publications
RD 31 jul 2024