Abstract
The gene expression data is characterized by its considerably great amount of features in comparison to the number of observations. The direct use of traditional statistics techniques of supervised classification can give poor results in gene expression data. Therefore, dimension reduction is recommendable prior to the application of a classifier. In this work, we propose a method that combines two types of dimension reduction techniques: feature selection and feature extraction. First, one of the following feature selection procedures: a univariate ranking based on the Kruskal-Wallis statistic test, the Relief, and recursive feature elimination (RFE) is applied on the dataset. After that, principal components are formed with the selected features. Experiments carried out on eight gene expression datasets using three classifiers: logistic regression, k-nn and rpart, gave good results for the proposed method.
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References
ACUNA, E. (2003), A Comparison of Filter and Wrapper Methods for Feature Selection in Supervised Classification, Proceedings of Interface Computing Science and Statistics, 35.
ACUNA, E., and RODRIGUEZ, C. (2005), Dprep: data preprocessing and visualization functions for classification. R package version 1.0. http://math.uprm.edu/edgar/dprep.html.
ALON, U., BARKAI, N., NOTTERMAN, D. A., GISH, K., et al. (1999). Broad patters of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. PNAS 96, 6745–6750.
ALIZADEH, A., EISEN, M., DAVIS, R., MA, C., LOSSOS, I., ROSENWALD, A., BOLDRICK, J., SABET, H., et al. (2000). Distinct types of diffuse large B-Cell-Lymphoma Identified by Gene Expression Profiling. Nature, 403, 503–511.
AMBROISE, C. and MCLACHLAN, G. (2002). Selection bias in gene extraction on the basis of microarray gene-expression data. PNAS vol. 99, 6562–6566.
BAIR, E., HASTIE, T., DEBASHIS, P., and TIBSHIRANI, R. (2004). Prediction by supervised principal components. Technical Report, Departament of Statistics, Stanford University
BRAGA-NETO, U. and DOUGHERTY, E. R. (2004). Is cross-validation valid for small-sample microarray classification?. Bioinformatics 20, 2465–2472.
DETTLING, M. and BUHLMANN, P. (2003). Boosting for Tumor Classification with Gene Expression Data. Bioinformatics, 19, 1061–1069.
DUDOIT, S., FRIDLYAND, J. and SPEED, T. (2002). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data. JASA,97, 77–87.
GOLUB, T., SLONIM, D., TAMAYO, P., HUARD, C., GASSENBEEK, M., et al. (1999). Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science, 286, 531–537.
GUYON, I. and ELISSEEFF, A. (2003). An introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182.
HEDENFALK, I., DUGGAN, D., CHEN, Y., RADMACHER, M., BITTNER, M., SIMON, R., MELTZER, P., et al. (2001). Gene-expression profiles in hereditary breast cancer. New England Journal of medicine 344, 539–548.
KHAN, J., WEI, J., RINGNER, M., SAAL, L., LADANYI, M., et al. (2001). Classification and Diagnostic Prediction of Cancer Using Gene Expression Profiling and Artificial Neural Networks. Nature Medicine, 6, 673–679.
NGUYEN, D.V. and ROCKE, D. M. (2002). Multi-Class Cancer Classification via Partial Least Squares with Gene Expression Profiles Bioinformatics, 18, 1216–1226.
NOTTERMAN, D. A., ALON, U., SIERK, A. J., et al. (2001). Trancriptional gene expression profiles of colorectal adenoma, adenocarcinoma, and normal tissue examined by oligonucleotide arrays. Cancer Research 61, 3124–3130.
POMEROY, S., TAMAYO, P., GAASENBEEK, M., STURLA, L., ANGELO, M., MCLAUGHLIN, M., et al. (2002). Prediction of Central Nervous System EmbryonalTumor Outcome Based on Gene Expression. Nature, 415, 436–442.
SINGH, D., FEBBO, P., ROSS, K., JACKSON, D., MANOLA, J., LADD, C., TAMAYO, P., RENSHAW, A., et al. (2002). Gene Expression Correlates of Clinical Prostate Cancer Behavior. Cancer Cell, 1, 203–209.
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Acuña, E., Porras, J. (2006). Improving the Performance of Principal Components for Classification of Gene Expression Data Through Feature Selection. In: Batagelj, V., Bock, HH., Ferligoj, A., Žiberna, A. (eds) Data Science and Classification. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-34416-0_35
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DOI: https://doi.org/10.1007/3-540-34416-0_35
Publisher Name: Springer, Berlin, Heidelberg
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