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BioMed Research International
Volume 2013 (2013), Article ID 387673, 10 pages
http://dx.doi.org/10.1155/2013/387673
Research Article

A Comparative Analysis of Biomarker Selection Techniques

Dipartimento di Matematica e Informatica, Università degli Studi di Cagliari, Via Ospedale 72, 09124 Cagliari, Italy

Received 23 April 2013; Revised 22 September 2013; Accepted 23 September 2013

Academic Editor: Eugénio Ferreira

Copyright © 2013 Nicoletta Dessì et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. A. Arthur , W. A. Colburn, V. G. DeGruttola et al., “Biomarkers and surrogate endpoints: preferred definitions and conceptual framework,” Clinical Pharmacology and Therapeutics, vol. 69, no. 3, pp. 89–95, 2001. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Saeys, I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507–2517, 2007. View at Publisher · View at Google Scholar · View at Scopus
  3. J. P. A. Ioannidis, “Microarrays and molecular research: noise discovery?” The Lancet, vol. 365, no. 9458, pp. 454–455, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Lai, M. J. T. Reinders, L. J. van't Veer, and L. F. A. Wessels, “A comparison of univariate and multivariate gene selection techniques for classification of cancer datasets,” BMC Bioinformatics, vol. 7, article 235, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. I. B. Jeffery, D. G. Higgins, and A. C. Culhane, “Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data,” BMC Bioinformatics, vol. 7, article 359, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. Z. He and W. Yu, “Stable feature selection for biomarker discovery,” Computational Biology and Chemistry, vol. 34, no. 4, pp. 215–225, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. W. Awada, T. M. Khoshgoftaar, D. J. Dittman, R. Wald, and A. Napolitano, “A review of the stability of feature selection techniques for bioinformatics data,” in Proceedings of the IEEE 13th International Conference on Information Reuse and Integration, pp. 356–363, 2012.
  8. A. Kalousis, J. Prados, and M. Hilario, “Stability of feature selection algorithms: a study on high-dimensional spaces,” Knowledge and Information Systems, vol. 12, no. 1, pp. 95–116, 2007. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Saeys, T. Abeel, and Y. Van de Peer, “Robust Feature Selection Using Ensemble Feature Selection Techniques,” in Proceedings of the European Conference ECML (PKDD '08), vol. 5212 of Lecture Notes in Artificial Intelligence, pp. 313–325, Springer, 2008.
  10. U. Alon, N. Barka, D. A. Notterman et al., “Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays,” Proceedings of the National Academy of Sciences of the United States of America, vol. 96, no. 12, pp. 6745–6750, 1999. View at Publisher · View at Google Scholar · View at Scopus
  11. T. R. Golub, D. K. Slonim, P. Tamayo et al., “Molecular classification of cancer: class discovery and class prediction by gene expression monitoring,” Science, vol. 286, no. 5439, pp. 531–527, 1999. View at Publisher · View at Google Scholar · View at Scopus
  12. D. Singh, P. G. Febbo, K. Ross et al., “Gene expression correlates of clinical prostate cancer behavior,” Cancer Cell, vol. 1, no. 2, pp. 203–209, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. R. Shen, A. Chinnaiyan, and D. Ghosh, “Pathway analysis reveals functional convergence of gene expression profiles in breast cancer,” BMC Medical Genomics, vol. 1, no. 1, article 28, 2008. View at Publisher · View at Google Scholar
  14. F. Reyal, M. H. van Vliet, N. J. Armstrong et al., “A comprehensive analysis of prognostic signatures reveals the high predictive capacity of the Proliferation, Immune response and RNA splicing modules in breast cancer,” Breast Cancer Research, vol. 10, no. 6, article R93, 2008. View at Publisher · View at Google Scholar · View at Scopus
  15. P. Wirapati, C. Sotiriou, S. Kunkel et al., “Meta-analysis of gene expression profiles in breast cancer: toward a unified understanding of breast cancer subtyping and prognosis signatures,” Breast Cancer Research, vol. 10, no. 4, article R65, 2008. View at Publisher · View at Google Scholar · View at Scopus
  16. http://www.geneontology.org.
  17. J. Z. Wang, Z. Du, R. Payattakool, P. S. Yu, and C.-F. Chen, “A new method to measure the semantic similarity of GO terms,” Bioinformatics, vol. 23, no. 10, pp. 1274–1281, 2007. View at Publisher · View at Google Scholar · View at Scopus
  18. I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, 3rd edition, 2011.
  19. U. M. Braga-Neto and E. R. Dougherty, “Is cross-validation valid for small-sample microarray classification?” Bioinformatics, vol. 20, no. 3, pp. 374–380, 2004. View at Publisher · View at Google Scholar · View at Scopus
  20. L. I. Kuncheva, “A Stability Index for Feature Selection,” in Proceedings of the International Multi-Conference Artificial Intelligence and Applications, ACTA Press, Anaheim, Calif, USA, 2007.
  21. L. M. Cannas, N. Dessì, and B. Pes, “Assessing similarity of feature selection techniques in high-dimensional domains,” Pattern Recognition Letters, vol. 34, no. 12, pp. 1446–1453, 2013.
  22. T. Fawcett, “ROC graphs: notes and practical considerations for researchers,” Tech. Rep. HPL-2003-4, HP Laboratories, 2003.
  23. C. Ambroise and G. J. McLachlan, “Selection bias in gene extraction on the basis of microarray gene-expression data,” Proceedings of the National Academy of Sciences of the United States of America, vol. 99, no. 10, pp. 6562–6566, 2002. View at Publisher · View at Google Scholar · View at Scopus
  24. H. Liu and R. Setiono, “Chi2: feature selection and discretization of numeric attributes,” in Proceedings of the IEEE 7th International Conference on Tools with Artificial Intelligence, pp. 388–391, November 1995. View at Scopus
  25. J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986. View at Publisher · View at Google Scholar · View at Scopus
  26. W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C, 1998.
  27. J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, Calif, USA, 1993.
  28. R. C. Holte, “Very simple classification rules perform well on most commonly used datasets,” Machine Learning, vol. 11, no. 1, pp. 63–91, 1993. View at Publisher · View at Google Scholar · View at Scopus
  29. I. Kononenko, “Estimating attributes: analysis and extensions of RELIEF,” in Proceedings of the European Conference on Machine Learning, pp. 171–182, 1994.
  30. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1-3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  31. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, “The WEKA data mining software: an update,” SIGKDD Explorations, vol. 11, no. 1, 2009.
  32. http://bioinformatics.clemson.edu/G-SESAME.
  33. X. Zhou and D. P. Tuck, “MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data,” Bioinformatics, vol. 23, no. 9, pp. 1106–1114, 2007. View at Publisher · View at Google Scholar · View at Scopus
  34. W. Altidor, T. M. Khoshgoftaar, and J. Van Hulse, “Robustness of filter-based feature ranking: a case study,” in Proceedings of the 24th International Florida Artificial Intelligence Research Society (FLAIRS '11), pp. 453–458, May 2011. View at Scopus
  35. N. Dessì and B. Pes, “An evolutionary method for combining different feature selection criteria in microarray data classification,” Journal of Artificial Evolution and Applications, vol. 2009, Article ID 803973, 10 pages, 2009. View at Publisher · View at Google Scholar
  36. J. Dutkowski and A. Gambin, “On consensus biomarker selection,” BMC Bioinformatics, vol. 8, supplement 5, article S5, 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. Y. Leung and Y. Hung, “A multiple-filter-multiple-wrapper approach to gene selection and microarray data classification,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 7, no. 1, pp. 108–117, 2010. View at Publisher · View at Google Scholar · View at Scopus
  38. T. Feng, F. Xuezheng, Z. Yanqing, and A. G. Bourgeois, “Improving feature subset selection using a genetic algorithm for microarray gene expression data,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '06), pp. 2529–2534, Vancouver, Canada, July 2006. View at Scopus
  39. P. Yang, B. B. Zhou, Z. Zhang, and A. Y. Zomaya, “A multi-filter enhanced genetic ensemble system for gene selection and sample classification of microarray data,” BMC Bioinformatics, vol. 11, supplement 1, article S5, 2010. View at Publisher · View at Google Scholar · View at Scopus