Abstract
Microarray experiments usually output small volumes but high dimensional data. Selecting a number of genes relevant to the tasks at hand is usually one of the most important steps for the expression data analysis. While numerous researches have demonstrated the effectiveness of gene selection from different perspectives, existing endeavors, unfortunately, ignore the data imbalance reality, where one type of samples (e.g., cancer tissues) may be significantly fewer than the other (e.g., normal tissues). In this paper, we carry out a systematic study to investigate the impact of gene selection on imbalanced microarray data. Our objective is to understand that if gene selection is applied to imbalanced expression data, what kind of consequences it may bring to the final results? For this purpose, we apply five gene selection measures to eleven microarray datasets, and employ four learning methods to build classification models from the data containing selected genes only. Our study will bring important findings and draw numerous conclusions on (1) the impact of gene selection on imbalanced data, and (2) behaviors of different learning methods on the selected data.
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References
Golub, T., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999)
Xiong, M., et al.: Biomarker identification by feature wrappers. Genome Research 11, 1878–1887 (2001)
Segal, E., et al.: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nature Genetics 34(2), 166–176 (2003)
Zhan, J., Deng, H.: Gene selection for classification of microarray data based on Bayes error. BMC Bioinfo. 8 (2007)
Diaz, R., Alvarez, S.: Gene selection and classification of microarray data using random forest. BMC Bioinfo. 7 (2006)
Mamitsuka, H.: Selecting features in microarray classification using ROC curves. Pattern Recognition 39, 2393–2404 (2006)
Li, T., et al.: A comparative study of feature selection and multiclass classification methods for tissue classification based on gene expression. Bioinformatics 20, 2429–2437 (2004)
Statnikov, A., et al.: A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21(5) (2005)
Kent Ridge, Kent Ridge Biomedical Data Set Repository, http://sdmc.i2r.a-star.edu.sg/rp/
Witten, Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, San Francisco (1999)
Yu, L., Liu, H.: Feature selection for high-dimensional data: A fast correlationbased filter solution. In: Proc. of ICML (2003)
Hall, M.A.: Correlation-based feature selection for discrete and numeric class machine learning. In: Proc. of ICML (2000)
Plackett, R.: Karl Pearson and the Chi-Squared Test. International Statistical Review 51(1), 59–72 (1983)
Quinlan, J.: C4.5: Programs for Machine learning. M. Kaufmann, San Francisco (1993)
Robnik, M., Kononenko, I.: Theoretical and empirical analysis of RelieF and RreliefF. Machine Learning 53, 23–69 (2003)
Cristianini, N., Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, Cambridge (2000)
Breiman, L.: Random Forests. Machine Learning (2001)
Aha, D., Kibler, D., Albert, M.: Instance-based learning algorithms. Machine learning 6, 37–66 (1991)
Pablo de Olavide, Pablo de Olavide University of Seville, Gene Expression Data Repository, http://www.upo.es/eps/bigs/datasets.html
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Kamal, A.H.M., Zhu, X., Pandya, A.S., Hsu, S., Shoaib, M. (2009). The Impact of Gene Selection on Imbalanced Microarray Expression Data. In: Rajasekaran, S. (eds) Bioinformatics and Computational Biology. BICoB 2009. Lecture Notes in Computer Science(), vol 5462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00727-9_25
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DOI: https://doi.org/10.1007/978-3-642-00727-9_25
Publisher Name: Springer, Berlin, Heidelberg
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