- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
BioMed Research International
Volume 2013 (2013), Article ID 478410, 10 pages
An Efficient Ensemble Learning Method for Gene Microarray Classification
Department of Computer Engineering, Islamic Azad University, Dezful Branch, Dezful 313, Iran
Received 30 April 2013; Accepted 12 July 2013
Academic Editor: Arnout Voet
Copyright © 2013 Alireza Osareh and Bita Shadgar. 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.
- P. O. Brown and D. Botstein, “Exploring the new world of the genome with DNA microarrays,” Nature Genetics, vol. 21, no. 1, pp. 33–37, 1999.
- J. Quackenbush, “Microarray data normalization and transformation,” Nature Genetics, vol. 32, no. 5, pp. 496–501, 2002.
- J. Khan, J. S. Wei, M. Ringnér et al., “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nature Medicine, vol. 7, no. 6, pp. 673–679, 2001.
- H. Bhaskar, D. C. Hoyle, and S. Singh, “Machine learning in bioinformatics: a brief survey and recommendations for practitioners,” Computers in Biology and Medicine, vol. 36, no. 10, pp. 1104–1125, 2006.
- 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.
- L. J. Van't Veer, H. Dai, M. J. Van de Vijver et al., “Gene expression profiling predicts clinical outcome of breast cancer,” Nature, vol. 415, no. 6871, pp. 530–536, 2002.
- R. Blanco, P. Larrañaga, I. Inza, and B. Sierra, “Gene selection for cancer classification using wrapper approaches,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 18, no. 8, pp. 1373–1390, 2004.
- S. Cho and H. Won, “Machine learning in DNA microarray analysis for cancer classification,” in Proceedings of the 1st Asia-Pacific Bioinformatics Conference on Bioinformatics, pp. 189–198, 2003.
- M. P. S. Brown, W. N. Grundy, D. Lin et al., “Knowledge-based analysis of microarray gene expression data by using support vector machines,” Proceedings of the National Academy of Sciences of the United States of America, vol. 97, no. 1, pp. 262–267, 2000.
- T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler, “Support vector machine classification and validation of cancer tissue samples using microarray expression data,” Bioinformatics, vol. 16, no. 10, pp. 906–914, 2000.
- N. Friedman, M. Linial, I. Nachman, and D. Pe'er, “Using Bayesian networks to analyze expression data,” in Proceedings of the 4th Annual International Conference on Computational Molecular Biology (RECOMB '00), pp. 127–135, April 2000.
- G. J. Gordon, R. V. Jensen, L.-L. Hsiao et al., “Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma,” Cancer Research, vol. 62, no. 17, pp. 4963–4967, 2002.
- X. Wang, M. J. Hessner, Y. Wu, N. Pati, and S. Ghosh, “Quantitatative quality control in microarray experiments and the application in data filtering, normalization and false positive rate prediction,” Bioinformatics, vol. 19, no. 11, pp. 1341–1347, 2003.
- Y. Peng, “A novel ensemble machine learning for robust microarray data classification,” Computers in Biology and Medicine, vol. 36, no. 6, pp. 553–573, 2006.
- T. Dietterich, “Ensemble methods in machine learning,” in Proceedings of the Multiple Classifier System Conference, pp. 1–15, 2000.
- T. G. Dietterich, “An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization,” Machine Learning, vol. 40, no. 2, pp. 139–157, 2000.
- P. Yang, Y. H. Yang, B. B. Zhou, and A. Y. Zomaya, “A review of ensemble methods in bioinformatics,” Current Bioinformatics, vol. 5, no. 4, pp. 296–308, 2010.
- L. K. Hansen and P. Salamon, “Neural network ensembles,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993–1001, 1990.
- C.-X. Zhang and J.-S. Zhang, “RotBoost: a technique for combining rotation forest and adaboost,” Pattern Recognition Letters, vol. 29, no. 10, pp. 1524–1536, 2008.
- C.-X. Zhang and J.-S. Zhang, “A variant of rotation forest for constructing ensemble classifiers,” Pattern Analysis and Applications, vol. 13, no. 1, pp. 59–77, 2010.
- K. W. De Bock, K. Coussement, and D. Van den Poel, “Ensemble classification based on generalized additive models,” Computational Statistics and Data Analysis, vol. 54, no. 6, pp. 1535–1546, 2010.
- K.-H. Liu and D.-S. Huang, “Cancer classification using Rotation Forest,” Computers in Biology and Medicine, vol. 38, no. 5, pp. 601–610, 2008.
- Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997.
- L. Breiman, “Bagging predictors,” Machine Learning, vol. 24, no. 2, pp. 123–140, 1996.
- L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
- S.-I. Lee and S. Batzoglou, “Application of independent component analysis to microarrays,” Genome Biology, vol. 4, no. 11, article r76, 2003.
- M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Transactions on Neural Networks, vol. 13, no. 6, pp. 1450–1464, 2002.
- P. Mitra, C. A. Murthy, and S. K. Pal, “Unsupervised feature selection using feature similarity,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 3, pp. 301–312, 2002.
- C.-H. Yang, L.-Y. Chuang, and C.-H. Yang, “IG-GA: a hybrid filter/wrapper method for feature selection of microarray data,” Journal of Medical and Biological Engineering, vol. 30, no. 1, pp. 23–28, 2010.
- L. Yu and H. Liu, “Efficient feature selection via analysis of relevance and redundancy,” Journal of Machine Learning Research, vol. 5, no. 12, pp. 1205–1224, 2004.
- Z. Zhu, Y.-S. Ong, and M. Dash, “Markov blanket-embedded genetic algorithm for gene selection,” Pattern Recognition, vol. 40, no. 11, pp. 3236–3248, 2007.
- T. Li, C. Zhang, and M. Ogihara, “A comparative study of feature selection and multiclass classfication methods for tissue classification based on gene expression,” Bioinformatics, vol. 20, no. 15, pp. 2429–2437, 2004.
- I. Kononenko, “Estimating attributes: analysis and extensions of RELIEF,” in Proceedings of the European Conference on Machine Learning, pp. 171–182, 1994.
- K. Kira and L. Rendell, “A practical approach to feature selection,” in Proceedings of the 9th International Workshop on Machine Learning, pp. 249–256, 1992.
- V. Bolón-Canedo, N. Sánchez-Maroño, and A. Alonso-Betanzos, “An ensemble of filters and classifiers for microarray data classification,” Pattern Recognition, vol. 45, no. 1, pp. 531–539, 2012.
- C. Ding and H. Peng, “Minimum redundancy feature selection from microarray gene expression data,” in Proceedings of the 2nd IEEE Computational Systems Bioinformatics, pp. 523–528, 2003.
- L. Kuncheva and J. Rodriguez, “An experimental study on rotation forest ensembles,” in Multiple Classifier Systems, vol. 4472 of Lecture Notes on Computer Science, pp. 459–468, 2007.
- 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.
- 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.
- D. Margineantu and T. Detterich, “Pruning adaptive boosting,” in Proceedings of the 14th International Conference of Machine Learning, pp. 378–387, 1997.
- L. Kuncheva, Combining Pattern Classifiers, John Wiley & Sons, 2004.