Modelling gene interaction networks from time-series gene expression data using evolving spiking neural networks
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ORIGINAL PAPER
Modelling gene interaction networks from time‑series gene expression data using evolving spiking neural networks Elisa Capecci1 · Jesus L. Lobo2 · Ibai Laña2 · Josafath I. Espinosa‑Ramos1 · Nikola Kasabov1 Received: 28 June 2018 / Accepted: 10 January 2019 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
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
1 Introduction In the era of big data, processing complex information that belongs to a high-dimensional space is a common challenge for the research community (Angelov and Kasabov * Elisa Capecci [email protected] Jesus L. Lobo [email protected] Ibai Laña [email protected] Josafath I. Espinosa‑Ramos [email protected] Nikola Kasabov [email protected] 1
Knowledge Engineering and Discovery Research Institute of the Auckland University of Technology, AUT Tower, cnr Rutland and Wakefield Street, Auckland, New Zealand
Tecnalia Research and Innovation, P. Tecnologico Bizkaia, Ed. 700, 48160 Derio, Spain
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2005; Trevisan et al. 2010; Kelly et al. 2010; Angelov et al. 2010; Angelov and Yager 2013; Trevisan et al. 2014). Novel artificial intelligence techniques have been developed that can learn complex big data and produce accurate results. These techniques process the information available using the same mechanisms involved in memory formation and synaptic plasticity in the human brain and are called spiking neural network (SNN) tech
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