Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
López, M., Mallorquín, P., Vega, M.: Aplicaciones de los microarrays y biochips en salud humana: Informe de vigilancia tecnológica. Genoma España (2005)
Fodor, I.: A survey of dimension reduction techniques. Technical report (2002)
Dai, J.J., Lieu, L., Rocke, D.: Dimension Reduction for Classification with Gene Expression Microarray Data. Stat. App in Gen. and Mol. Bio. 5(1), 1–21 (2006)
Golub, T.R., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531–537 (1999)
Tang, E.K., et al.: Feature selection for microarray data using least squares svm and particle swarm optimization. In: Proceedings of IEEE, CIBCB 2005, pp. 1–8 (2005)
Alba, E., et al.: Gene selection in cancer classification using pso/svm and ga/svm hybrid algorithms. In: IEEE CEC 2007, pp. 284–290 (2007)
Pearson, K.: On lines and planes of closest fit to systems of points in space. Philosophical Magazine 2(6), 559–572 (1901)
Karaboga, D.: An idea based on honey bee swarm for numerical optimization. Technical report, Computer Engineering Department, Engineering Faculty, Erciyes University (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Garro, B.A., Vazquez, R.A., Rodríguez, K. (2014). Classification of DNA Microarrays Using Artificial Bee Colony (ABC) Algorithm. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8794. Springer, Cham. https://doi.org/10.1007/978-3-319-11857-4_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-11857-4_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11856-7
Online ISBN: 978-3-319-11857-4
eBook Packages: Computer ScienceComputer Science (R0)