Optimization for feature selection in DNA microarrays

Heuristics: Theory and Applications

Volumn , Issue , 2013, Pages 311-324

  Gardeux, Vincent ^a   Natowicz, René ^b   Wanderley, Maria Fernanda Barbosa ^c   Chelouah, Rachid ^a  

^a UNIVERSITÉ DE CERGY PONTOISE   (France)

^b ESIEE PARIS   (France)

^c FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

Bioinformatics; DNA microarray analysis; Feature selection; Oncology; Optimization

Indexed keywords

EID: 84887104091     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Chapter

References (26)

1. U. Alon, N. Barkai, D. A. Notterman, K. Gish, S. Ybarra, D. Mack, and A. J. Levine. 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 (PNAS), 96(12):6745-6750, 1999.
2. 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 (PNAS), 99(10):6562-6566, 2002.
3. R. E. Bellman. Adaptive Control -A Guided Tour. Princeton University Press, 1961.
4. A. Berchuck, E. S. Iversen, J. M. Lancaster, J. Pittman, J. Luo, P. Lee, S. Murphy, H. K. Dressman, P. G. Febbo, M. West, et al. Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers. Clinical cancer research, ll(10):3686-3696, 2005.
5. J. M. Deutsch. Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics, 19(l):45-52, 2003.
6. S. Dudoit, J. Fridlyand, and T. P. Speed. Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Sta-tistical Association, 97(457):77-87, 2002.
7. A. Dupuy and R. M. Simon. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. Journal of the National Cancer Institute, 99(2):147-157, 2007.
8. V. Gardeux, R. Chelouah, P. Siarry, and F. Glover. Unidimensional search for solving continuous high-dimensional optimization problems. In Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications, pages 1096-1101, Pisa, Italy, November 30-December 2, 2009. IEEE Computer Society.
9. V. Gardeux, R. Natowicz, R. Chelouah, R. Rouzier, A. Padua Braga, and P. Siarry. Un algorithme d'optimisation à haute dimension pour la fouille de données: méthode et application en onco-pharmacogénomique. In Proceedings of Recherche Opérationnelle et Aide à la Decision, Saint-Etienne, France, March 2-4, 2011.
10. B. Ghattas and A. Ben Ishak. Selection de variables pour la classification binaire en grande dimension: comparaisons et application aux données de biopuces. Journal de la société frangaise de statistique, 149(3):43-66, 2008.
11. T. R. Golub, D. K. Slonim, P. Tamayo, C. Huard, M. Gaasenbeek, J. P. Mesirov, H. Coller, M. L. Loh, J. R. Downing, M. A. Caligiuri, and C. D. Bloomfield. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286(5439):531-537, 1999.
12. Isabelle Guyon and André Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157-1182, 2003.
13. K. R. Hess, K. Anderson, W. F. Symmans, V. Valero, N. Ibrahim, J. A. Mejia, D. Booser, R. L. Theriault, A. U. Buzdar, P. J. Dempsey, et al. Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. Journal of clinical oncology, 24(26):4236-4244, 2006.
14. L. D. Miller, J. Smeds, J. George, V. B. Vega, L. Vergara, A. Ploner, Y. Pawitan, P. Hall, S. Klaar, E. T. Liu, and J. Bergh. An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proceedings of the National Academy of Sciences of the United States of America, 102(38):13550-13555, September 2005.
15. R. Natowicz, R. Incitti, B. C. Euler Horta, P. Guinot, K. Yan, C. Coutant, F. Andre, L. Pusztai, and R. Rouzier. Prediction of the outcome of preoperative chemotherapy in breast cancer using DNA probes that provide information on both complete and incomplete responses. BMC bioinformatics, 9(149), 2008.
16. C. Orsenigo. Gene selection and cancer microarray data classification via mixed-integer optimization. Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, 4973:141-152, 2008.
17. N. Pochet, F. De Smet, J. A. K. Suykens, and B. L. R. De Moor. Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. Bioinformatics, 20(17):3185-3195, 2004.
18. S. L. Pomeroy, P. Tamayo, M. Gaasenbeek, L. M. Sturla, M. Angelo, M. E. McLaughlin, J. Y. H. Kim, L. C. Goumnerova, P. M. Black, C. Lau, et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature, 415(6870):436-442, 2002.
19. A. Rakotomamonjy. Variable selection using SVM based criteria. The Journal of Machine Learning Research, 3:1357-1370, 2003.
20. S. Ramaswamy, K. N. Ross, E. S. Lander, and T. R. Golub. A molecular signature of metastasis in primary solid tumors. Nature genetics, 33(l):49-54, 2002.
21. S. Shah and A. Kusiak. Cancer gene search with data-mining and genetic algorithms. Computers in Biology and Medicine, 37(2):251-261, 2007.
22. M. A. Shipp, K. N. Ross, P. Tamayo, A. P. Weng, J. L. Kutok, R. C. T. Aguiar, M. Gaasenbeek, M. Angelo, M. Reich, G. S. Pinkus, et al. Diffuse large b-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nature medicine, 8(l):68-74, 2002.
23. R. Simon, M. D. Radmacher, K. Dobbin, and L. M. McShane. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. Journal of the National Cancer Institute, 95(1):14-18, 2003.
24. D. Singh, P. G. Febbo, K. Ross, D. G. Jackson, J. Manola, C. Ladd, P. Tamayo, A. A. Renshaw, A. V. D'Amico, J. P. Richie, et al. Gene expression correlates of clinical prostate cancer behavior. Cancer cell, l(2):203-209, 2002.
25. Adel Tabchy, Vicente Valero, Tatiana Vidaurre, Ana Lluch, Henry Gomez, Miguel Martin, Yuan Qi, Luis Javier Barajas-Figueroa, Eduardo Souchon, Charles Coutant, Franco D. Doimi, Nuhad K. Ibrahim, Yun Gong, Gabriel N. Hortobagyi, Kenneth R. Hess, W. Fraser Symmans, and Lajos Pusztai. Evaluation of a 30-gene paclitaxel, fluorouracil, doxorubicin, and cyclophosphamide chemotherapy response pre-dictor in a multicenter randomized trial in breast cancer. Clinical Cancer Research, 16(21):5351-5361, 2010.
26. J. Weston, A. Elisseeff, B. Scholkopf, and M. Tipping. Use of the zero norm with linear models and kernel methods. The Journal of Machine Learning Research, 3(7-8): 1439-1461, 2003.