Have a personal or library account? Click to login

Data mining methods for gene selection on the basis of gene expression arrays

Open Access
|Sep 2014

References

  1. 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
  2. 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
  3. De Rinaldis, E. (2007). DNA Microarrays: Current Applications, Horizon Scientific Press, Norfolk.
  4. Duda, R., Hart, P. and Stork, P. (2003). Pattern Classification and Scene Analysis, John Wiley, New York, NY.
  5. 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
  6. 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.
  7. 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
  8. 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
  9. Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection, Journal of Machine Learning Research 3(3): 1158-1182.
  10. 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
  11. Haykin, S. (1999). Neural Networks. A Comprehensive Foundation, 2nd Edition, Prentice-Hall, Englewood Cliffs, NJ.
  12. 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
  13. 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
  14. 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
  15. 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
  16. Makinaci, M. (2007). Support vector machine approach for classification of cancerous prostate regions, World Academy of Science, Engineering and Technology 1(7): 166-169.
  17. Matlab (2012). Matlab User Manual-Statistics Toolbox, MathWorks, Natic.
  18. 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
  19. 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
  20. 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
  21. Scholkopf, B. and Smola, A. (2002). Learning with Kernels, MIT Press, Cambridge, MA.
  22. 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.
  23. Tan, P.N., Steinbach, M. and Kumar, V. (2006). Introduction to Data Mining, Pearson Education, Boston, MA.
  24. Vanderbilt (2002). Data base of prostate cancer, Vanderbilt University, http://discover1.mc.vanderbilt.edu/discover/public/mcsvm.
  25. 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
  26. Wang, X. and Gotoh, O. (2009). Cancer classification using single genes, Genom Informatics 23(1): 179-188.10.1142/9781848165632_0017
  27. 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
  28. 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
  29. 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
  30. 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
DOI: https://doi.org/10.2478/amcs-2014-0048 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 657 - 668
Submitted on: Sep 17, 2013
Published on: Sep 25, 2014
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2014 Michał Muszyński, Stanisław Osowski, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.