Classification of DNA microarrays using artificial neural networks and ABC algorithm

DNA microarray is an efficient new technology that allows to analyze, at the same time, the expression level of millions of genes. The gene expression level indicates the synthesis of different messenger ribonucleic acid (mRNA) molecule in a cell. Using this gene expression level, it is possible to diagnose diseases, identify tumors, select the best treatment to resist illness, detect mutations among other processes. In order to achieve that purpose, several computational techniques such as pattern classification approaches can be applied. The classification problem consists in identifying different classes or groups associated with a particular disease (e.g., various types of cancer, in terms of the gene expression level). However, the enormous quantity of genes and the few samples available, make difficult the processes of learning and recognition of any classification technique. Artificial neural networks (ANN) are computational models in artificial intelligence used for classifying, predicting and approximating functions. Among the most popular ones, we could mention the multilayer perceptron (MLP), the radial basis function neural network (RBF) and support vector machine (SVM). The aim of this research is to propose a methodology for classifying DNA microarray. The proposed method performs a feature selection process based on a swarm intelligence algorithm to find a subset of genes that best describe a disease. After that, different ANN are trained using the subset of genes. Finally, four different datasets were used to validate the accuracy of the proposal and test the relevance of genes to correctly classify the samples of the disease. © 2015 Elsevier B.V. All rights reserved.