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http://dx.doi.org/10.5351/KJAS.2013.26.3.389

Screening and Clustering for Time-course Yeast Microarray Gene Expression Data using Gaussian Process Regression  

Kim, Jaehee (Department of Statistics, Duksung Women's University)
Kim, Taehoun (Department of PrePharmMed, Duksung Women's University)
Publication Information
The Korean Journal of Applied Statistics / v.26, no.3, 2013 , pp. 389-399 More about this Journal
Abstract
This article introduces Gaussian process regression and shows its application with time-course microarray gene expression data. Gene screening for yeast cell cycle microarray expression data is accomplished with a ratio of log marginal likelihood that uses Gaussian process regression with a squared exponential covariance kernel function. Gaussian process regression fitting with each gene is done and shown with the nine top ranking genes. With the screened data the Gaussian model-based clustering is done and its silhouette values are calculated for cluster validity.
Keywords
Differentially expressed; Gaussian process regression; log marginal likelihood; squared exponentia covariance function; time-course microarray expression data; yeast;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Eckel, J. E., Gennings, C., Chinchilli, V. M., Burgoon, L. D. and Zacharewski, T. R. (2004). Empirical Bayes gene screening tool for time-course or dose-response microarray data, Journal of Biopharmaceutical Statistics, 14, 647-670.   DOI   ScienceOn
2 Fraley, C. and Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation, Journal of the American Statistical Association, 97, 611-631.   DOI   ScienceOn
3 Fraley, C. and Raftery, A. E. (2006). MCLUST Version 3 for R: Normal mixture modeling and model-based clustering. Technical Report No. 504.
4 Fraley, C. and Raftery, A. E. (2007). Bayesian regularization for normal mixture estimation and model-based clustering, Journal of Classication, 24, 155-181.
5 Hero, A. O., Fleury, G., Mears, A. J. and Swaroop, A. (2004). Multicriteria gene screening for analysis of differential expression with DNA microarrays, Journal on Applied Signal processing, 2004, 43-52.   DOI   ScienceOn
6 Kalaitzis, A. and Lawrence, N. (2011). A simple approach to ranking differentially expressed gene expression time courses through Gaussian process regression, BMC Bioinformatics, 12, 180.   DOI
7 Kim, J. and Kim, H. (2008). Clustering of change using Fourier coefficient, Bioinformatics, 24, 184-191.   DOI   ScienceOn
8 Lee, K., Kim, T. and Kim, J. (2011). Gene screening and clustering of yeast microarray gene expression data, The Korean Journal of Applied Statistics, 24, 1077-1094.   과학기술학회마을   DOI   ScienceOn
9 Ma, S. (2006). Empirical study of supervised gene screening, BMC Bioinformatics, 7, 537.   DOI
10 Rasmussen, C. E. and Williams, C. K. (2005). Gaussian Processes for Machine Learning, MIT Press
11 Rousseeuw, P. T. (1987). Silhouettes: Graphical aid to the interpretation and validation of cluster analysis. Journal of Computation Applied Math, 20, 53-65.   DOI   ScienceOn
12 Serban, N. and Wasserman, L. (2005). CATS: Clustering after transformation and smoothing, Journal of the American Statistical Association, 471, 990-999.
13 Spellman, P. T., Sherlock, G., Zhang, M. Q., Iyer, V. R., Anders, K., Eisen, M. B., Brown, P. O., Botstein, D. and Futcher, B. (1998). Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization, Molecular Biology of the Cell, 9, 3273-3297.   DOI   ScienceOn
14 Toronen, R., Kolehmainen, M., Wong, G. and Castren, E. (1999). Analysis of gene expression data using self-organizing maps, Federation of European Biochemical Societies, 451, 142-146.   DOI   ScienceOn
15 Zhang, L., Zhang, A. and Ramanathan, M. (2003). Fourier harmonic approach for visualizing temporal patterns of gene expression data, IEEE Computer Society Bioinformatics Conference, 2, 137-147.