Selection of Characteristics and Classification of DNA Microarrays Using Bioinspired Algorithms and the Generalized Neuron
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.
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 \(Prostate\ cancer\).
KeywordsMicroarrays Genetic algorithms PSO Differential evolution Neural networks Pattern recognition
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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