Spiking Neural Models and Their Application in DNA Microarrays Classification

Gene expression in DNA microarrays has been widely used to determine which genes are related with a disease, identify tumors, determine a treatment for a disease, etc.; all of this based on the classification of DNA microarrays. Several pattern recognition and computational intelligence techniques such as Artificial Neural Networks (ANN) have been used to predict diseases in terms of the gene expression levels. However, to train an ANN using a reduced number of DNA microarray samples, it is necessary to apply more robust neural models due to the samples are composed of an enormous quantity of genes. In this paper, we described how a spiking neural model can be applied to solve a DNA microarrays classification task. The proposed methodology selects the most relevant genes that are related with a particular disease by means of the artificial bee colony algorithm. After that, the selected genes are used to train a spiking neural model (SNM) using a learning approach based on the particle swarm optimization algorithm. Finally, to asses the accuracy of the proposed methodology, a DNA microarrays dataset related to identify leukemia was used. The experimental results obtained with the proposed methodology are compared against those obtained with traditional distance classifier, feedforward neural networks and support vector machines. The results suggest that SNM provides acceptable results and comparable with those obtained with classical neural networks.

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