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 netrtificial Bee Colony algorithm work (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 Published by Elsevier B.V. 37 38 39 40 41 42 43 44 45 46 47 . Introduction DNA microarray is an essential technique in molecular biolgy that allows, at the same time, to know the expression level f millions of genes. The DNA microarray consists in immobilizing known deoxyribonucleic acid (DNA) molecule layout in a glass ontainer and then this information with other genetic informaion are hybridized. This process is the base to identify, classify or redict diseases such as different kind of cancer [1–4]. The process to obtain a DNA microarray is based on the comination of a healthy DNA reference with a testing DNA. Using Please cite this article in press as: B.A. Garro, et al., Classification of DNA Appl. Soft Comput. J. (2015), http://dx.doi.org/10.1016/j.asoc.2015.10. uorophores and a laser it is possible to generate a color spot matrix nd obtain quantitative values that represent the expression level f each gene [5]. This expression level is like a signature useful to ∗ Corresponding author. Tel.: +52 5556223899. E-mail addresses: beatriz.garro@iimas.unam.mx (B.A. Garro), atya.rodriguez@iimas.unam.mx (K. Rodríguez), ravem@lasallistas.org.mx R.A. Vázquez). ttp://dx.doi.org/10.1016/j.asoc.2015.10.002 568-4946/© 2015 Published by Elsevier B.V. 48 49 50 51 52 diagnose different diseases. Furthermore, it can be used to identify genes that modify their genetic expression when a medical treatment is applied, identify tumors and genes that make regulation genetic networks, detect mutations among other applications [6]. Computational techniques combined with DNA microarrays can generate efficient results. The classification of DNA microarrays can be divided into three stages: gene finding, class discovery, and class prediction [7,8]. The DNA microarray samples have millions of genes and selecting the best genes set in such a way that get a trustworthy classification is a difficult task. Nonetheless, the evolutionary and bio-inspired algorithms, such as genetic algorithm (GA) [9], particle swarm optimization (PSO) [10], bacterial foraging algorithm (BFA) [11] and fish school search (FSS) [12], are excellent options to solve this problem. However, the performance of these algorithms depends of the fitness function, the parameters of the algorithm, the search space complexity, convergence, etc. In microarrays using artificial neural networks and ABC algorithm, 002 general, the performance of these algorithms is very similar among them, but depends of adjusting carefully their parameters. Based on that, the criterion that we used to select the algorithm for finding the set of most relevant genes was in term of the number of 53 54 55 56