Classification of DNA Microarrays Using Artificial Bee Colony (ABC) Algorithm

• Published in: Advances in Swarm Intelligence pp. 207 - 214
• Main Authors: Garro, Beatriz Aurora, Vazquez, Roberto Antonio, Rodríguez, Katya
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2014
• Series: Lecture Notes in Computer Science
• Subjects: Artificial Bee Colony (ABC) algorithm; DNA microarrays; feature selection; pattern classification; PCA technique
• ISBN: 9783319118567; 3319118560
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-11857-4_24

DNA microarrays are a powerful technique in genetic science due to the possibility to analyze the gene expression level of millions of genes at the same time. Using this technique, it is possible to diagnose diseases, identify tumours, select the best treatment to resist illness, detect mutations and prognosis purpose. However, the main problem that arises when DNA microarrays are analyzed with computational intelligent techniques is that the number of genes is too big and the samples are too few. For these reason, it is necessary to apply pre-processing techniques to reduce the dimensionality of DNA microarrays. In this paper, we propose a methodology to select the best set of genes that allow classifying the disease class of a gene expression with a good accuracy using Artificial Bee Colony (ABC) algorithm and distance classifiers. The results are compared against Principal Component Analysis (PCA) technique and others from the literature.