Abstract
DNA microarrays are used for the massive quantification of gene expression. This analysis allows to diagnose, identify and classify different diseases. This is a computationally challenging task due to the large number of genes and a relatively small number of samples.
Some papers applied the generalized neuron (GN) to solve approximation functions, to calculate density estimates, prediction and classification problems [1, 2].
In this work we show how a GN can be used in the task of microarray classification. The proposed methodology is as follows: first reducing the dimensionality of the genes using a genetic algorithm, then the generalized neuron is trained using one bioinspired algorithms: Particle Swarm Optimization, Genetic Algorithm and Differential Evolution. Finally the precision of the methodology it is tested by classifying three databases of DNA microarrays: \(Leukemia\ benchmarck\) \(ALL-AML\), \(Colon\ Tumor\) and \(Prostate\ cancer\).
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Acknowledgments
The authors thank the IIMAS headquarters Mrida and Dr. Ernesto Perez Rueda for their valuable comments. Alejandra Romero thanks CONACYT for the scholarship received.
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Romero-Montiel, F.A., Rodríguez-Vázquez, K. (2018). Selection of Characteristics and Classification of DNA Microarrays Using Bioinspired Algorithms and the Generalized Neuron. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_7
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DOI: https://doi.org/10.1007/978-3-030-04491-6_7
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