Abstract
Gene expression can be used for profiling of cancer cell state and classification of disease. Some cancer variants have been attributed to one or few significant gene expression features. This paper investigates the combination of novel features selection methods - Minimum-Redundancy, Maximum-Relevance - and artificial neural networks - the spiking neural network NeuCube architecture - for genomic data classification and analysis. A NeuCube model performs not only a better classification than other machine learning methods, but most importantly contributes to the feature extraction and marker discovery along with providing gene interaction network analysis for selected genes. Results demonstrated that the methodology proposed could contribute to bioinformatics data analysis for the treatment of disease by discovery of new biomarkers from gene expression data.
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Acknowledgments
EC has been funded by the Auckland University of Technology (AUT) SRIF INTERACT project 2017-18 and by the Knowledge Engineering and Discovery Research Institute (KEDRI, www.kedri.aut.ac.nz). Many thanks to Amanda Dixon-McIver of IGENZ Ltd, Auckland, New Zealand for contributing to the paper. Several people have contributed to the research that resulted in this paper, especially: Dr Y. Chen, Dr J. Hu, L. Zhou and Dr E. Tu. 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.
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Dray, J., Capecci, E., Kasabov, N. (2018). Spiking Neural Networks for Cancer Gene Expression Time Series Modelling and Analysis. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11301. Springer, Cham. https://doi.org/10.1007/978-3-030-04167-0_57
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DOI: https://doi.org/10.1007/978-3-030-04167-0_57
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