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BioMed Research International
Volume 2013 (2013), Article ID 387673, 10 pages
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.
- 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.
- Y. Saeys, I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics,” Bioinformatics, vol. 23, no. 19, pp. 2507–2517, 2007.
- J. P. A. Ioannidis, “Microarrays and molecular research: noise discovery?” The Lancet, vol. 365, no. 9458, pp. 454–455, 2005.
- 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.
- 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.
- Z. He and W. Yu, “Stable feature selection for biomarker discovery,” Computational Biology and Chemistry, vol. 34, no. 4, pp. 215–225, 2010.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques, Elsevier, 3rd edition, 2011.
- 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.
- 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.
- 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.
- T. Fawcett, “ROC graphs: notes and practical considerations for researchers,” Tech. Rep. HPL-2003-4, HP Laboratories, 2003.
- 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.
- 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.
- J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986.
- W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical Recipes in C, 1998.
- J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann, San Francisco, Calif, USA, 1993.
- R. C. Holte, “Very simple classification rules perform well on most commonly used datasets,” Machine Learning, vol. 11, no. 1, pp. 63–91, 1993.
- I. Kononenko, “Estimating attributes: analysis and extensions of RELIEF,” in Proceedings of the European Conference on Machine Learning, pp. 171–182, 1994.
- 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.
- 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.
- 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.
- 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.
- 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.
- J. Dutkowski and A. Gambin, “On consensus biomarker selection,” BMC Bioinformatics, vol. 8, supplement 5, article S5, 2007.
- 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.
- 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.
- 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.