Abstract
The paper presents a methodology to assess the problems behind static gene expression data modelling and analysis with machine learning techniques. As a case study, transcriptomic data collected during a longitudinal study on the effects of diet on the expression of oxidative phosphorylation genes was used. Data were collected from 60 abdominally overweight men and women after an observation period of eight weeks, whilst they were following three different diets. Real-valued static gene expression data were encoded into spike trains using Gaussian receptive fields for multinomial classification using an evolving spiking neural network (eSNN) model. Results demonstrated that the proposed method can be used for predictive modelling of static gene expression data and future works are proposed regarding the application of eSNNs for personalised modelling.
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Acknowledgments
The presented study was a collaboration between the Knowledge Engineering and Discovery Research Institute (KEDRI, https://kedri.aut.ac.nz/) funded by the Auckland University of Technology of New Zealand and the University of Trento in Italy. Several people have contributed to the research that resulted in this paper, especially: Y. Chen, J. Hu, E. Tu and L. Zhou.
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Shahi, G.K., Bilbao, I., Capecci, E., Nandini, D., Choukri, M., Kasabov, N. (2018). Analysis, Classification and Marker Discovery of Gene Expression Data with Evolving Spiking Neural Networks. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11305. Springer, Cham. https://doi.org/10.1007/978-3-030-04221-9_46
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