A Comparative Study of Statistical and Artificial Intelligence based Classification Algorithms on Central Nervous System Cancer Microarray Gene Expression Data

Yıl 2016, Cilt: 4 Sayı: Special Issue-1, 78 - 81, 26.12.2016

Mustafa Turan Arslan , Adem Kalinli

https://doi.org/10.18201/ijisae.267094

Cited By: 5

Öz

A variety of methods are used in order to classify cancer gene expression profiles based on microarray data. Especially, statistical methods such as Support Vector Machines (SVM), Decision Trees (DT) and Bayes are widely preferred to classify on microarray cancer data. However, the statistical methods can often be inadequate to solve problems which are based on particularly large-scale data such as DNA microarray data. Therefore, artificial intelligence-based methods have been used to classify on microarray data lately. We are interested in classifying microarray cancer gene expression by using both artificial intelligence based methods and statistical methods. In this study, Multi-Layer Perceptron (MLP), Radial basis Function Network (RBFNetwork) and Ant Colony Optimization Algorithm (ACO) have been used including statistical methods. The performances of these classification methods have been tested with validation methods such as v-fold validation. To reduce dimension of DNA microarray gene expression has been used Correlation-based Feature Selection (CFS) technique. According to the results obtained from experimental study, artificial intelligence-based classification methods exhibit better results than the statistical methods.

Anahtar Kelimeler

DNA microarray, Feature selection, Classification, Statistical methods, Artificial intelligence methods, Gene Expression Data

Kaynakça

• H. Liu, I. Bebu, and X. Li, “Microarray probes and probe sets.,” Front Biosci (Elite Ed), vol. 2, pp. 325–38, 2010.
• H. U. Luleyap, The Principles of Moleculer Genetics. Izmir: Nobel Bookstore, 2008.
• K. Ipekdal, “Microarray Technology,” 2011. [Online]. Available: http://yunus.hacettepe.edu.tr/~mergen/sunu/s_mikroarrayandecology.pdf. [Accessed: 05-Jul-2016].
• M. a. Hall and L. a. Smith, “Practical feature subset selection for machine learning,” Comput Sci, vol. 98, pp. 181–191, 1998.
• V. Vapnik and V. Vapnik, Statistical learning theory. 1998.
• I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene Selection for Cancer Classification using Support Vector Machines,” Mach Learn, vol. 46, no. 1/3, pp. 389–422, 2002.
• J. Novakovic, M. Minic, and A. Veljovic, “Genetic Search for Feature Selection in Rule Induction Algorithms,” pp. 1109–1112, 2010.
• C. Saylan, “Intelligent method based on new feature selection algorithm on renal transplantation patients,” Kadir Has University, 2013.
• E. Oztemel, Artificial Neural Network. Papatya Publishing, 2003.
• E. Cetin, “The Applications of Artificial Intelligence,” Ankara,Turkey: Seckin Publishing, 2007, pp. 379–401.
• D. S. Broomhead and D. Lowe, “Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks,” 1988.
• F. M. Ham and I. Kostanic, Principles of neurocomputing for science and engineering. McGraw Hill, 2001.
• A. C. Marco Dorigo, V. Maniezzo, Alberto Colorni, Marco Dorigo, Marco Dorigo, Vittorio Maniezzo, Vittorio Maniezzo, Alberto Colorni, “Positive Feedback as a Search Strategy,” 1991.
• B. Alatas and E. Akin, “The Discovery of Classification Rules by Ant Colony Algorithm,” 2004.
• R. S. Parpinelli, H. S. Lopes, and A. A. Freitas, “An ant colony algorithm for classification rule discovery,” in Data mining: A heuristic approach, 2002, pp. 191–208.
• B. Liu, H. A. Abbass, and B. Mckay, “Classification Rule Discovery with Ant Colony Optimization,” IEEE Comput Intell Bull , vol. 3, no. 1, pp. 31–35, 2004.
• S. L. Pomeroy, P. Tamayo, J. M. Olson, T. Curran, C. Wetmor, D. N. Louis, J. P. Mesirov, E. S. Lander, and T. R. G. Ii, “Prediction of central nervous system embryonal tumour outcome based on gene expression,” vol. 415, no. January, pp. 436–442, 2002.
• “Central Nervous System Cancer Dataset,” 2013. [Online]. Available: http://bioinformatics.rutgers.edu/Static/Supplements/CompCancer/Affymetrix/pomeroy-2002-v1/pomeroy-2002-v1_database.txt. [Accessed: 12-Jul-2016].
• E. Frank, M. Hall, L. Trigg, G. Holmes, and I. H. Witten, “Data mining in bioinformatics using Weka,” Bioinformatics, vol. 20, no. 15, pp. 2479–2481, Oct. 2004.