Abstract
A challenging task in time series microarray data analysis is to identify co-expressed groups of genes from a large input space. The overall objective of this study is to obtain knowledge about the most important genes and clusters related to production and growth rate in a real-world microarray data analysis task. Various measures are engaged to evaluate the importance of each gene and to group genes based on their correlation with the output and each other. Some strategies for grouping and selecting genes are integrated resulting in several models tested for real biological data. All proposed models are tested on a real microarray data analysis problem and the results obtained are throughtly presented as well as interpreted from a biological perspective.
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References
Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007)
Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J.A., Armañanzas, R., Santafé, G., Pérez, A., Robles, V.: Machine learning in bioinformatics. Briefings in Bioinformatics 7(1), 86–112 (2006)
Lee, C.-P., Leu, Y.: A novel hybrid feature selection method for microarray data analysis. Applied Soft Computing 11, 208–213 (2011)
Peddada, S.D., Lobenhofer, E.K., Li, L., Afshari, C.A., Weinberg, C.R., Umbach, D.M.: Gene selection and clustering for time-course and doseresponse microarray experiments using order-restricted inference. Bioinformatics 19(7), 834–841 (2003)
Ernst, J., Bar-Joseph, Z.: Stem: a tool for the analysis of short time series gene expression data. BMC Bioinformatics 7(1), 191 (2006)
Storey, J.D., Xiao, W., Leek, J.T., Tompkins, R.G., Davis, R.W.: Significance analysis of time course microarray experiments. Proceedings of the National Academy of Sciences of the United States of America 102(36), 12837–12842 (2005)
Liu, T., Lin, N., Shi, N., Zhang, B.: Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments. BMC Bioinformatics 10(1), 146 (2009)
Liu, H., Liu, L., Zhang, H.: Ensemble gene selection by grouping for microarray data classification. Journal of Biomedical Informatics 43, 81–87 (2010)
Lu, Y., Han, J.: Cancer classification using gene expression data. Information Systems 28(4), 243–268 (2003)
Wang, Y., Tetko, I.V., Hall, M.A., Frank, E., Facius, A., Mayer, K.F.X., Mewes, H.W.: Gene selection from microarray data for cancer classification—a machine learning approach. Computational Biology and Chemistry 29, 37–46 (2005)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Learning 27(8), 1226–1238 (2005)
Bolboaca, S.-D., Jantschi, L.: Pearson versus spearman, kendall’s tau correlation analysis on structure-activity relationships of biologic active compounds. Leonardo Journal of Sciences (9), 179–200 (2006)
Smyth, G.K., Speed, T.: Normalization of cdna microarray data. Methods 31(4), 265–273 (2003)
Prieto, C., Risueno, A., Fontanillo, C., De Las Rivas, J.: Human gene coexpression landscape: Confident network derived from tissue transcriptomic profiles. PLoS One 3(12), e3911 (2008)
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Chira, C., Sedano, J., Villar, J.R., Prieto, C., Corchado, E. (2014). Gene Clustering in Time Series Microarray Analysis. In: Herrero, Á., et al. International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing, vol 239. Springer, Cham. https://doi.org/10.1007/978-3-319-01854-6_30
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DOI: https://doi.org/10.1007/978-3-319-01854-6_30
Publisher Name: Springer, Cham
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