Modelling gene interaction networks from time-series gene expression data using evolving spiking neural networks

  • Elisa CapecciEmail author
  • Jesus L. Lobo
  • Ibai Laña
  • Josafath I. Espinosa-Ramos
  • Nikola Kasabov
Original Paper


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.


Artificial intelligence Evolving spiking neural networks Transcriptome Gene expression Microarray Data analysis Gene interaction networks Nickel allergy Allergic contact dermatitis 



Several people have contributed to the research that resulted in this work, especially: Dr. Y. Chen, Dr. J. Hu, L. Zhou, Dr. E. Tu and Maryam Gholami-Doborjeh. Many thanks to Caitlin Veale and Kate Steckmest for proofreading the manuscript. Funding was provided by Auckland University of Technology, New Zealand (SRIF INTERACT 2017-18).


  1. Angelov P, Filev DP, Kasabov N (2010) Evolving intelligent systems: methodology and applications, vol 12. Wiley, New YorkCrossRefGoogle Scholar
  2. Angelov P, Kasabov N (2005) Evolving computational intelligence systems. In: Proceedings of the 1st international workshop on genetic fuzzy systems, pp 76–82Google Scholar
  3. Angelov P, Yager R (2013) Density-based averaging-a new operator for data fusion. Inf Sci 222:163–174MathSciNetzbMATHCrossRefGoogle Scholar
  4. Argyriou A, Evgeniou T, Pontil M (2007) Multi-task feature learning. In: Advances in neural information processing systems, pp 41–48Google Scholar
  5. Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M et al (2012) Ncbi geo: archive for functional genomics data sets—update. Nucleic Acids Res 41(D1):D991–D995CrossRefGoogle Scholar
  6. Beer DG, Kardia SL, Huang CC, Giordano TJ, Levin AM, Misek DE, Lin L, Chen G, Gharib TG, Thomas DG et al (2002) Gene-expression profiles predict survival of patients with lung adenocarcinoma. Nat Med 8(8):816CrossRefGoogle Scholar
  7. Beleites C, Neugebauer U, Bocklitz T, Krafft C, Popp J (2013) Sample size planning for classification models. Anal Chim Acta 760:25–33CrossRefGoogle Scholar
  8. Bradley PS, Mangasarian OL (1998) Feature selection via concave minimization and support vector machines. In: ICML, vol 98, pp 82–90Google Scholar
  9. Capano V, Herrmann HJ, de Arcangelis L (2015) Optimal percentage of inhibitory synapses in multi-task learning. Sci Rep 5:9895CrossRefGoogle Scholar
  10. Capecci E, Kasabov N, Wang GY (2015) Analysis of connectivity in neucube spiking neural network models trained on eeg data for the understanding of functional changes in the brain: A case study on opiate dependence treatment. Neural Netw 68:62–77CrossRefGoogle Scholar
  11. Causton H, Quackenbush J, Brazma A (2009) Microarray gene expression data analysis: a beginner’s guide. Wiley, New YorkGoogle Scholar
  12. Chen Y, Hu J, Kasabov N, Hou ZG, Cheng L (2013) Neucuberehab: a pilot study for eeg classification in rehabilitation practice based on spiking neural networks. Neural Inf Process 8228:70–77Google Scholar
  13. Chung FR (1997) Spectral graph theory, vol 92. American Mathematical Society, ProvidencezbMATHGoogle Scholar
  14. DeVries T, Taylor GW (2017) Dataset augmentation in feature space. In: International conference on learning representations. arXiv preprint. arXiv:1702.05538
  15. Dobbin KK, Simon RM (2006) Sample size planning for developing classifiers using high-dimensional dna microarray data. Biostatistics 8(1):101–117zbMATHCrossRefGoogle Scholar
  16. Dobbin KK, Zhao Y, Simon RM (2008) How large a training set is needed to develop a classifier for microarray data? Clin Cancer Res 14(1):108–114CrossRefGoogle Scholar
  17. Doborjeh MG, Kasabov N, Doborjeh ZG (2017) Evolving, dynamic clustering of spatio/spectro-temporal data in 3d spiking neural network models and a case study on EEG data. In: Evolving systems, pp 1–17Google Scholar
  18. Edgar R, Domrachev M, Lash AE (2002) Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30(1):207–210CrossRefGoogle Scholar
  19. Espinosa-Ramos JI, Capecci E, Kasabov N (2017) A computational model of neuroreceptor dependent plasticity (NRDP) based on spiking neural networks. In: IEEE transactions on cognitive and developmental systems.
  20. Ezkurdia I, Juan D, Rodriguez JM, Frankish A, Diekhans M, Harrow J, Vazquez J, Valencia A, Tress ML (2014) Multiple evidence strands suggest that there may be as few as 19,000 human protein-coding genes. Hum Mol Genet 23(22):5866–5878CrossRefGoogle Scholar
  21. Ferreira J, Ferro M, Fernandes B, Valenca M, Bastos-Filho C, Barros P (2017) Extreme learning machine autoencoder for data augmentation. In: 2017 IEEE Latin American conference on computational intelligence (LA-CCI). IEEE, pp 1–6Google Scholar
  22. Feuk L, Carson AR, Scherer SW (2006) Structural variation in the human genome. Nat Rev Genet 7(2):85–97CrossRefGoogle Scholar
  23. Fürnkranz J (2002) Round robin classification. J Mach Learn Res 2(Mar):721–747MathSciNetzbMATHGoogle Scholar
  24. Gerstner W (1995) Time structure of the activity in neural network models. Phys Rev E 51(1):738MathSciNetCrossRefGoogle Scholar
  25. Gerstner W (2001) Plausible neural networks for biological modelling, what’s different with spiking neurons?. Kluwer Academic Publishers, DordrechtGoogle Scholar
  26. Gerstner W, Sprekeler H, Deco G (2012) Theory and simulation in neuroscience. Science 338(6103):60–65CrossRefGoogle Scholar
  27. Ghosh-Dastidar S, Adeli H (2007) Improved spiking neural networks for eeg classification and epilepsy and seizure detection. Integr Comput Aided Eng 14(3):187–212CrossRefGoogle Scholar
  28. He X, Cai D, Niyogi P (2006) Laplacian score for feature selection. In: Advances in neural information processing systems, pp 507–514Google Scholar
  29. Hebb DO (1949) The organization of behavior: a neuropsychological approach. Wiley, New YorkGoogle Scholar
  30. Huang S, Cai N, Pacheco PP, Narandes S, Wang Y, Xu W (2018) Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genom Proteom 15(1):41–51Google Scholar
  31. Izhikevich EM (2006) Polychronization: computation with spikes. Neural Comput 18(2):245–282MathSciNetzbMATHCrossRefGoogle Scholar
  32. Kasabov N (2012) Neucube evospike architecture for spatio-temporal modelling and pattern recognition of brain signals. In: Mana N, Schwenker F, Trentin E (eds) Artificial neural networks in pattern recognition, lecture notes in computer science, vol 7477. Springer, Berlin, pp 225–243CrossRefGoogle Scholar
  33. Kasabov N (2014) Neucube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw 52:62–76CrossRefGoogle Scholar
  34. Kasabov N, Capecci E (2015) Spiking neural network methodology for modelling, recognition and understanding of EEG spatio-temporal data measuring cognitive processes during mental tasks. Inf Sci 294:565–575CrossRefGoogle Scholar
  35. Kasabov N, Dhoble K, Nuntalid N, Indiveri G (2013) Dynamic evolving spiking neural networks for on-line spatio- and spectro-temporal pattern recognition. Neural Netw 41:188–201CrossRefGoogle Scholar
  36. Kasabov N, Scott NM, Tu E, Marks S, Sengupta N, Capecci E, Othman M, Doborjeh MG, Murli N, Hartono R et al (2016) Evolving spatio-temporal data machines based on the neucube neuromorphic framework: design methodology and selected applications. Neural Netw 78:1–14CrossRefzbMATHGoogle Scholar
  37. Kelly JG, Angelov PP, Trevisan J, Vlachopoulou A, Paraskevaidis E, Martin-Hirsch PL, Martin FL (2010) Robust classification of low-grade cervical cytology following analysis with ATR–FTIR spectroscopy and subsequent application of self-learning classifier eclass. Anal Bioanal Chem 398(5):2191–2201CrossRefGoogle Scholar
  38. Koefoed L, Capecci E, Kasabov N (2018) Analysis of gene expression time series data of ebola vaccine response using the neucube and temporal feature selection. In: 2018 international joint conference on neural networks (IJCNN). IEEE, pp 1–7.
  39. Kohane IS, Butte AJ, Kho A (2002) Microarrays for an integrative genomics. MIT Press, CambridgeCrossRefGoogle Scholar
  40. Kumar C, Mann M (2009) Bioinformatics analysis of mass spectrometry-based proteomics data sets. FEBS Lett 583(11):1703–1712CrossRefGoogle Scholar
  41. Maass W (1997) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10(9):1659–1671CrossRefGoogle Scholar
  42. Marks S (2017) Immersive visualisation of 3-dimensional spiking neural networks. Evol Syst 8(3):193–201. MathSciNetCrossRefGoogle Scholar
  43. McLachlan G, Do KA, Ambroise C (2005a) Analyzing microarray gene expression data, vol 422. Wiley, New YorkzbMATHGoogle Scholar
  44. McLachlan GJ, Do KA, Ambroise C (2005b) Analyzing microarray gene expression data. Wiley series in probability and statistics. Wiley, New York. CrossRefGoogle Scholar
  45. Møller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533CrossRefGoogle Scholar
  46. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B (2008) Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat Methods 5(7):621–628CrossRefGoogle Scholar
  47. Mukherjee S, Tamayo P, Rogers S, Rifkin R, Engle A, Campbell C, Golub TR, Mesirov JP (2003) Estimating dataset size requirements for classifying DNA microarray data. J Comput Biol 10(2):119–142CrossRefGoogle Scholar
  48. Nuntalid N, Dhoble K, Kasabov N (2011) EEG classification with BSA spike encoding algorithm and evolving probabilistic spiking neural network. In: Lu BL, Zhang L, Kwok J (eds) Neural information processing, lecture notes in computer science, vol 7062. Springer, Berlin, pp 451–460CrossRefGoogle Scholar
  49. Olshausen BA, Field DJ (1997) Sparse coding with an overcomplete basis set: a strategy employed by v1? Vis Res 37(23):3311–3325CrossRefGoogle Scholar
  50. Panda S, Sato TK, Hampton GM, Hogenesch JB (2003) An array of insights: application of dna chip technology in the study of cell biology. Trends Cell Biol 13(3):151–156CrossRefGoogle Scholar
  51. Pedersen MB, Skov L, Menné T, Johansen JD, Olsen J (2007) Gene expression time course in the human skin during elicitation of allergic contact dermatitis. J Investig Dermatol 127(11):2585–2595CrossRefGoogle Scholar
  52. Pertea M, Salzberg SL (2010) Between a chicken and a grape: estimating the number of human genes. Genome Biol 11(5):206. CrossRefGoogle Scholar
  53. The MathWorks Inc. (2018a) Statistics and machine learning toolbox: user’s guide release 2012b. Accessed June 2018
  54. The MathWorks Inc. (2018b) Statistics and machine learning toolbox: user’s guide release 2012b. Accessed June 2018
  55. Radovic M, Ghalwash M, Filipovic N, Obradovic Z (2017) Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinform 18(1):9CrossRefGoogle Scholar
  56. Roffo G (2018) Feature selection library [version 6.0.2018]. Accessed June 2018
  57. Roffo G, Melzi S, Castellani U, Vinciarelli A (2017) Infinite latent feature selection: a probabilistic latent graph-based ranking approach. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1398–1406Google Scholar
  58. Roffo G, Melzi S, Cristani M (2015) Infinite feature selection. In: 2015 IEEE international conference on computer vision (ICCV), pp 4202–4210.
  59. Schrauwen B, Van Campenhout J (2003) BSA, a fast and accurate spike train encoding scheme. In: Proceedings of the international joint conference on neural networks, vol 4. IEEE, Piscataway, pp 2825–2830Google Scholar
  60. Sebastiani P, Gussoni E, Kohane IS, Ramoni MF (2003) Statistical challenges in functional genomics. Stat Sci 18(1):33–70. MathSciNetzbMATHCrossRefGoogle Scholar
  61. Shen EH, Overly CC, Jones AR (2012) The allen human brain atlas: comprehensive gene expression mapping of the human brain. Trends Neurosci 35(12):711–714CrossRefGoogle Scholar
  62. Song S, Miller KD, Abbott LF (2000) Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci 3(9):919–926. CrossRefGoogle Scholar
  63. Sun X, Zou Y, Nikiforova V, Kurths J, Walther D (2010) The complexity of gene expression dynamics revealed by permutation entropy. BMC Bioinform 11(1):607. CrossRefGoogle Scholar
  64. Tapia JE, Perez CA (2013) Gender classification based on fusion of different spatial scale features selected by mutual information from histogram of LBP, intensity, and shape. IEEE Trans Inf Forens Secur 8(3):488–499CrossRefGoogle Scholar
  65. Thorpe SJ, Gautrais J (1998) Rank order coding. In: Computational neuroscience. Springer, pp 113–118Google Scholar
  66. Tomašev N, Buza K, Marussy K, Kis PB (2015) Hubness-aware classification, instance selection and feature construction: survey and extensions to time-series. In: Feature selection for data and pattern recognition. Springer, pp 231–262Google Scholar
  67. Trevisan J, Angelov PP, Patel II, Najand GM, Cheung KT, Llabjani V, Pollock HM, Bruce SW, Pant K, Carmichael PL et al (2010) Syrian hamster embryo (she) assay (ph 6.7) coupled with infrared spectroscopy and chemometrics towards toxicological assessment. Analyst 135(12):3266–3272CrossRefGoogle Scholar
  68. Trevisan J, Park J, Angelov PP, Ahmadzai AA, Gajjar K, Scott AD, Carmichael PL, Martin FL (2014) Measuring similarity and improving stability in biomarker identification methods applied to fourier-transform infrared (FTIR) spectroscopy. J Niophoton 7(3–4):254–265Google Scholar
  69. Tu E, Cao L, Yang J, Kasabov N (2014) A novel graph-based k-means for nonlinear manifold clustering and representative selection. Neurocomputing 143:109–122CrossRefGoogle Scholar
  70. Tu E, Kasabov N, Othman M, Li Y, Worner S, Yang J, Jia Z (2014) Neucube(st) for spatio-temporal data predictive modelling with a case study on ecological data. In: 2014 international joint conference on neural networks (IJCNN), pp 638–645.
  71. Tu E, Kasabov N, Yang J (2017) Mapping temporal variables into the neucube for improved pattern recognition, predictive modeling, and understanding of stream data. IEEE Trans Neural Netw Learn Syst 28(6):1305–1317MathSciNetCrossRefGoogle Scholar
  72. Verleysen M, François D (2005) The curse of dimensionality in data mining and time series prediction. In: International work-conference on artificial neural networks. Springer, pp 758–770Google Scholar
  73. Wang X, Wu M, Li Z, Chan C (2008) Short time-series microarray analysis: methods and challenges. BMC Syst Biol 2(1):58CrossRefGoogle Scholar
  74. Wit E, McClure J (2004) Statistics for microarrays: design, analysis and inference. Wiley, New York. zbMATHCrossRefGoogle Scholar
  75. Zhou F, De la Torre F (2012) Factorized graph matching. In: 2012 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 127–134Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Knowledge Engineering and Discovery Research Institute of the Auckland University of TechnologyAucklandNew Zealand
  2. 2.Tecnalia Research and InnovationDerioSpain

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