[Baldi, P. and Long, A. (2001). A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inference of gene changes, Bioinformatics 17(4): 509-519.10.1093/bioinformatics/17.6.50911395427]Search in Google Scholar
[Chang, C.-C. and Lin, C.-J. (2011). LibSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology 1(27): 1-27.10.1145/1961189.1961199]Search in Google Scholar
[De Rinaldis, E. (2007). DNA Microarrays: Current Applications, Horizon Scientific Press, Norfolk.]Search in Google Scholar
[Duda, R., Hart, P. and Stork, P. (2003). Pattern Classification and Scene Analysis, John Wiley, New York, NY.]Search in Google Scholar
[Eisen, M., Spellman, P. and Brown, P. (1998). Cluster analysis and display of genome wide expression patterns, Proceedings of the National Academy of Sciences 95(25): 14863-14868.10.1073/pnas.95.25.14863245419843981]Search in Google Scholar
[Fan, R.-E., Chen, P.-H. and Lin, C.-J. (2005). Working set selection using second order information for training SVM, Journal of Machine Learning Research 6(12): 1889-1918.]Search in Google Scholar
[Furey, T., Cristianini, N., Duffy, N., Bednarski, D., Schummer, M. and Haussler, D. (2000). Support vector machine classification and validation of cancer tissue samples using microarray expression data, Bioinformatics 16(10): 906-914.10.1093/bioinformatics/16.10.90611120680]Search in Google Scholar
[Golub, T., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A. and Bloomfield, C.D. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, Science 286(5439): 531-537.10.1126/science.286.5439.53110521349]Search in Google Scholar
[Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection, Journal of Machine Learning Research 3(3): 1158-1182.]Search in Google Scholar
[Guyon, I., Weston, A., Barnhill, S. and Vapnik, V. (2002). Gene selection for cancer classification using SVM, Machine Learning 46(1-3): 389-422.10.1023/A:1012487302797]Search in Google Scholar
[Haykin, S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Edition, Prentice-Hall, Englewood Cliffs, NJ.]Search in Google Scholar
[Herrero, J., Valencia, A. and Dopazon, A. (2001). A hierarchical unsupervised growing neural network for clustering gene expression patterns, Bioinformatics 17(2): 126-136.10.1093/bioinformatics/17.2.12611238068]Search in Google Scholar
[Hewett, R. and Kijsanayothin, P. (2008). Tumor classification ranking from microarray data, BMC Genomics 9(2): 1-11.10.1186/1471-2164-9-S2-S21255988618831787]Search in Google Scholar
[Huang, T.M. and Kecman, V. (2005). Gene extraction for cancer diagnosis by support vector machines-an improvement, Artificial Intelligence in Medicine 9(35): 185-194.10.1016/j.artmed.2005.01.00616026974]Search in Google Scholar
[Huang, X. and Pan, W. (2003). Linear regression and two-class classification with gene expression data, Bioinformatics 19(16): 2072-2078.10.1093/bioinformatics/btg28314594712]Search in Google Scholar
[Makinaci, M. (2007). Support vector machine approach for classification of cancerous prostate regions, World Academy of Science, Engineering and Technology 1(7): 166-169.]Search in Google Scholar
[Matlab (2012). Matlab User Manual-Statistics Toolbox, MathWorks, Natic.]Search in Google Scholar
[Mitsubayashi, H., Aso, S., Nagashima, T. and Okada, Y. (2008). Accurate and robust gene selection for desease classification using a simple statistics, Biomedical Informatics 3(2): 68-71.10.6026/97320630003068263795419238233]Search in Google Scholar
[Ramaswamy, S., Tamayo, P., Rifkin, R., Mukherjee, S., Yeang, C., Angelo, M., Ladd, C., Reich, M., Latulippe, E., Mesirov, J., Poggio, T., Gerald, W., Loda, M., Lander, E. and Golub, T. (2001). Multiclass cancer diagnosis using tumor gene expression signatures, Proceedings of the National Academy of Sciences 98(26): 15149-15154.10.1073/pnas.2115663986499811742071]Search in Google Scholar
[Sabo, K. (2014). Center-based l1-clustering method, International Journal of Applied Mathematics and Computer Science 24(1): 151-163, DOI: 10.2478/amcs-2014-0012.10.2478/amcs-2014-0012]Search in Google Scholar
[Scholkopf, B. and Smola, A. (2002). Learning with Kernels, MIT Press, Cambridge, MA.]Search in Google Scholar
[Sprent, P. and Smeeton, N. (2007). Applied Nonparametric Statistical Methods, Chapman and Hall-CRC, Boca Raton, FL. ´S winiarski, R.W. (2001). Rough sets methods in feature reduction and classification, International Journal of Applied Mathematics and Computer Science 11(3): 565-582.]Search in Google Scholar
[Tan, P.N., Steinbach, M. and Kumar, V. (2006). Introduction to Data Mining, Pearson Education, Boston, MA.]Search in Google Scholar
[Vanderbilt (2002). Data base of prostate cancer, Vanderbilt University, http://discover1.mc.vanderbilt.edu/discover/public/mcsvm.]Search in Google Scholar
[Vert, J. (2007). Kernel methods in genomics and computational biology, in G. Camps-Valls, J.L. Rojo-Alvarez and M. Martinez-Ramon (Eds.), Kernel Methods in Bioengineering, Signal and Image Processing, Idea Group, London, pp. 42-64.10.4018/978-1-59904-042-4.ch002]Search in Google Scholar
[Wang, X. and Gotoh, O. (2009). Cancer classification using single genes, Genom Informatics 23(1): 179-188.10.1142/9781848165632_0017]Search in Google Scholar
[Wang, X. and Gotoh, O. (2010). A robust gene selection method for microarray-based cancer classification, Cancer Informatics 9(2): 15-30.10.4137/CIN.S3794283437720234770]Search in Google Scholar
[Wiliński, A. and Osowski, S. (2012). Ensemble of data mining methods for gene ranking, Bulletin of the Polish Academy of Sciences 60(3): 461-471.10.2478/v10175-012-0058-x]Search in Google Scholar
[Woolf, P.J. and Wang, Y. (2000). A fuzzy logic approach to analyzing gene expression data, Physiological Genomics 3(1): 9-15.10.1152/physiolgenomics.2000.3.1.911015595]Search in Google Scholar
[Yang, F. (2011). Robust feature selection for microarray data based on multicriterion fusion, IEEE Transactions on Computational Biology and Bioinformatics 8(4): 1080-1092. 10.1109/TCBB.2010.10321566255]Search in Google Scholar