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응용통계연구 = The Korean journal of applied statistics, v.19 no.1, 2006년, pp.13 - 32
손인석 (고려대학교 통계학과) , 이재원 (고려대학교 통계학과) , 김서영 (전남대학교 기초과학연구소)
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Biologists are attempting to group genes based on the temporal pattern of gene expression levels. So far, a number of methods have been proposed for clustering microarray data. However, the results of clustering depends on the genes selection, therefore the gene selection with significant expression...
Barash, Y. and Friedman, N. (2002). Context-Specific Bayesian Clustering for Gene Expression Data, Journal of Computational Biology, 9, 169-191
Chen, G. et al. (2002). Evaluation and comparison of clustering algorithms in analyzing ES cell gene expression data, Statistica Sinica, 12, 241-262
Chu, S., DeRisi, J. et al., (1998). The transcriptional program of sporulation in budding yeast, Science, 282, 699-705
Datta, S. and Datta, S. (2003). Comparisons and validation of statistical clustering techniques for microarray gene expression data, Bioinformatics, 19, 459-466
Dudoit, S., Yang, Y. H., Speed, T. and Callow, M. J. (2002). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments, Statistica Sinica, 12, 111-139
Efron, B. (1982). The jackknife, the bootsrap, and other resampling plans, Society for industrial and applied mathematics
Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns, Proc. Natl Acad. Sci., 95, 14863-14868
Goldstein, D. R, Conlon, E. and Ghosh, D. (2002). Statistical issues in the clustering of gene expression data, Statistica Sinica, 12, 219-240
Ghosh, D. and Chinnaiyan, A. M. (2002). Mixture modelling of gene expression data from microarray experiments, Bioinformatics, 18, 275-286
Guthke, R, Schmidt-Heck, W., Hahn, D. and Pfaff, M. (2000). Gene expression data mining for functional genomics, Proceedings of European Symposium on Intelligent Techniques (EIST 2000), Aachen, Germany, 170-177
Hartigan, J. A. and Wong, M. A. (1979). A k-means clustering algorithm. Applied Statistics. Vol 28. 100-108
Hastie, T., Tibshirani, R et al. (2000). Gene shaving as a method for identifying distinct sets of genes with similar expression patterns, Genome Biology, 1, research003
Hihara, Y., Kamei, A., Kanehisa, M., Kaplan, A. and Ikeuchi, M. (2001). DNA microarray analysis of cyanobacterial gene expression during acclimation to high light, The Plant Cell, 13, 793-806
Hong, F. and Li, H. (2004). B-spline Based Empirical Bayes Methods for Identifying Genes with Different Time-course Expression Profiles. submitted
Kaufman, L. and Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis, New York, John Wiley
Kasturi, J., Acharya, R. and Ramanathan, R. (2003). An information theoretic approach for analyzing temporal patterns of gene expression, Bioinformatics, 19, 449-458
Kerr, M. K. and Churchill, G. A. (2001). Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments, Proc. Natl Acad. Sci., 98, 8961-8965
Kim, S. Y., Choi, T. M. and Bae J. S. (2005). Fuzzy types clustering for microarray data, International Journal of Computational Intelligence, bf 2, 12-15
Kim, S. Y., Lee, J. W. and Bae J. S. (2006). Effect of data normalization on fuzzy clustering of DNA microarray data., BMC Bioinformatics, To appear
Kim, S. Y., Lee, J. W. and Shon, I. S. (2006). Comparison of various statistical methods for identifying differential gene expression in replicated microarray data, Statistical Methods in Medical Research, 15, 1-18
Laura, L. and Owen, A. (2002). Plaid models for gene expression data, Statistica Sinica, 12, 61-86
Lonnstedt, I. and Speed, T. P. (2002). Replicated microarray data, Statistica Sinica, 12, 31-46
Luan, Y. and Li, H. (2003). Clustering of time-course gene expression data using a mixedeffects model with B-splines, Bioinformatics, 19, 474-482
McLachlan, G. J., Bean, R. W. and Peel, D. (2002). A mixture model based approach to the clustering of microarray expression data, Bioinformatics, 18, 1-10
Moon et al. (2002). Mice Lacking Paternally Expressed Pref-1/Dlk1 Display Growth Retardation and Accelerated Adiposity, Molecular and Cellular Biology, 22, 5585-5592
Smyth, G. K., Yang, Y. H. and Speed, T. (2003). Statistical issues in cDNA microarray data analysis, in Functional Genomics: Methods and Protocols, eds
Spellman, P. T., Sherlock, G., Zhang, M. Q. et al., (1998). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization, Mol. Biol. Cell., 12, 3273-3297
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S. and Golub, T. R. (1999). Interpreting patterns of gene expression with selforganizing maps:methods and application to hematopoietic differentiation, Proceedings of the National Academy of Sciences, 96, 2907-2912
Tusher, V., Tibshirani, R., and Chu, G. (2001). Significance analysis of microarrays applied to the ionizing radiation response, Proceedings of the National Academy of Sciences, 98, 5116-5124
Waddell, P. and Kishino, H. (2000). Cluster inference methods and graphical models evaluated on NC160 microarray gene expression data, Genome Informatics, 11, 129-140
Yeung, K., Haynor, D. R. and Ruzzo, W. L. (2001). Validating clustering for gene expression data, Bioinformatics, 17, 309-318
Yeung, K. Y., Fraley, C. Murua, A, Raftery, E. and Ruzzo, W. L. (2001). Model based clustering and data transformations for gene expression data, Bioinformatics, 17, 977-987
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