A New Generalized Neuron Model Applied to DNA Microarray Classification

The DNA Microarray classification played an important role in bioinformatics and medicine area. By means of the genetic expressions obtained from a DNA microarrays, it is possible to identify which genes are correlated to a particular disease, in order solve different tasks such as tumor detection, best treatment selection, etc. In the last years, several computational intelligence techniques have been proposed to identify different groups of genes associated with a particular disease; one popular example is the application of artificial neural networks (ANN). The main disadvantage of using this technique is that ANN require a representative number of samples to provide acceptable results. However, the enormous quantity of genes and the few samples available for any disease, demand the use of more robust artificial neural models, capable of providing acceptable results using few samples during the learning process. In this research, we described a new type of generalized neuron model (GNM) applied to the DNA microarray classification task. The proposed methodology selects the set of genes that better describe the disease applying the artificial bee colony algorithm; after that, the GNM is trained using the discovered genes by means of a differential evolution algorithm. Finally, the accuracy of the proposed methodology is evaluated classifying two types of cancer using DNA microarrays: the acute lymphocytic leukemia and the acute myeloid leukemia.