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References

  • Aach, J. and Church, G. M. (2001). Alignment gene expression time series with time warping algorithms. Bioinformatics 17, 495508.
  • Adler, R. J. (1981). The Geometry of Random Fields. Wiley, Chichester.
  • Angulo, J. M. and Ruiz-Medina, M. D. (1999). Multiresolution approximation to the stochastic inverse problem. Advances in Applied Probability 31, 10391057.
  • Berlinet, A., Biau, G. and Rouviére, L. (2008). Functional supervised classification with wavelets. Annales de l'ISUP 52, 6180.
  • Breyne, P. and Zabeau, M. (2001). Genome-wide expression analysis of plant cell cycle modulated genes. Current Opinion in Plant Biology 4, 136142.
  • Briones, M. R. S. and Bosco, F. (2009). Decoherence in yeast cell populations and its implications for genome-wide expression noise. Genetics and Molecular Research 8, 4751.
  • Cardot, H. and Sarda, P. (2005). Estimation in generalized linear models for functional data via penalized likelihood. Journal of Multivariate Analysis 92, 2441.
  • Cho, R. J., Campbell, M. J., Winzeler, E. A., Steinmetz, L., Conway, A., Wodicka, L., Wolfsberg, T. G., Gabrielian, A. E., Landsman, D., Lockhart, D. J. and Davis, R. W. (1998). A genome-wide transcriptional analysis of the mitotic cell cycle. Molecular Cell 2, 6573.
  • Cho, R. J., Huang, M., Campbell, M. J., Dong, H., Steinmetz, L., Sapinoso, L., Hampton, G., Elledge, S. J., Davis, R. W. and Lockhart, D. J. (2001). Transcriptional regulation and function during the human cell cycle. Nature Genetics 27, 4854.
  • Cohen, A., Daubechies, I. and Feauveau, J. C. (1992). Biorthogonal bases of compactly supported wavelets. Communications on Pure and Applied Mathematics 45, 485560.
  • Dobson, A. J. and Barnett, A. G. (2008). An Introduction to Generalized Linear Models. Chapman & Hall, New York.
  • Draghici, S. (2003). Data Analysis Tools for DNA Microarrays. Chapman & Hall, New York.
  • Eisen, M., Spellman, P. T., Brown, P. O. and Botstein, D. (1998). Cluster analysis and display genomewide expression patterns. Proceedings of the National Academy of Sciences USA 95, 1486314868.
  • Ewens, W. J. and Grant, G. R. (2005). Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health). Springer, New York.
  • Fan, J. and Gijbels, I. (1996). Local Polynomial Modelling and its Applications. Chapman & Hall, London.
  • Ferraty, F. and Vieu, P. (2006). Nonparameric Functional Data Analysis. Springer, New York.
  • Hall, P., Poskitt, D. and Presnell, B. (2001). A functional data-analytic approach to signal discrimination. Technometrics 43, 19.
  • Hestilow, T. J. and Huang, Y. (2009). Clustering of gene expression data based on shape similarity. Journal on Bioinformatics and Systems Biology. DOI: 10.1155/2009/195712.
  • Holter, N. S., Maritan, A., Cieplak, M., Fedoroff, N. V. and Banavar, J. R. (2001). Dynamic modeling of gene expression data. Proceedings of the National Academy of Sciences 98, 1693.
  • James, G. M. and Hastie, T. J. (2001). Functional linear discriminant analysis for irregular sampled curves. Journal of the Royal Statistical Society: Series B 63, 533550.
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  • James, G. M., Wang, J. and Zhu, J. (2009). Functional linear regression, that's interpretable. Annals of Statistics 37, 20832108.
  • Kaufman, L. and Rousseeuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York.
  • Kelbert, M., Leonenko, N. N. and Ruiz-Medina, M. D. (2005). Fractional random fields associated with stochastic fractional heat equations. Advances in Applied Probability 37, 108133.
  • Kim, S., Kim, J. K. and Choi, S. (2008). Independent arrays or independent time courses for gene expression time series data analysis. Neurocomputing 71, 2377.
  • Klevecz, R. R. (2000) Dynamic architecture of the yeast cell cycle uncovered by wavelet decomposition of expression microarray data. Functional and Integrative Genomics 1, 186192.
  • Laub, M. T., McAdams, H. H., Feldblyum, T., Fraser, C. M. and Shapiro, L. (2000). Global analysis of the genetic network controlling a bacterial cell cycle. Science 290, 21442148.
  • Leng, X. and Müller, H. G. (2006a). Classification using functional data analysis for temporal gene expression data. Bioinformatics 22, 6876.
  • Leng, X. and Müller, H. G. (2006b). Time ordering of gene co-expression. Biostatistics 7, 569584.
  • Liu, X. L. and Müller, H. G. (2003). Modes and clustering for time-warped gene expression profile data. Bioinformatics 19, 19371944.
  • McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models. Chapman & Hall, London.
  • Müller, H. (1987). Weighted local regression and kernel methods for nonparametric curve fitting. Journal of the American Statistical Association 82, 231238.
  • Müller, H. G. (2005). Functional modelling and classification of longitudinal data. Scandinavian Journal of Statistics 32, 223240.
  • Müller, H. G., Chiou, J. M. and Leng, X. (2008). Inferring gene expression dynamics via functional regression analysis. BMC Bioinformatics, 60.
  • Müller, H. G. and Stadtmüller, U. (2005). Generalized functional linear models. Annals of Statistics 33, 774805.
  • Peng, X., Karuturi, R. K., Miller, L. D., Lin, K., Jia, Y., Kondu. P., Wang, L., Wong, L. S., Liu, E. T., Balasubramanian, M. K. and Liu, J. (2005). Identification of cell cycle-regulated genes in fission yeast. Molecular Biology of the Cell 16, 10261042.
  • Picard, R. and Cook, D. (1984). Cross-validation of regression models. Journal of the American Statistical Association 79, 575583.
  • Rice, J. and Wu, C. (2000). Nonparametric mixed effects models for unequally sampled noisy curves. Biometrics 57, 253259.
  • Ruiz-Medina, M. D., Angulo, J. M. and Anh, V. V. (2003). Fractional generalized random fields on bounded domains. Stochastic Analysis and Applications 21, 465492.
  • Rustici, G., Mata, J., Kivinen, K., Lió, P., Penkett, C. J., Burns, G., Hayles, J., Brazma, A., Nurse, P. and Bähler, J. (2004). Identification of cell cycle-regulated genes in fission yeast. Nature Genetics 36, 809817.
  • Schuchhardt, J., Beule, D., Wolski, E. and Eickhoff, H. (2000). Normalization strategies for cDNA microarrays. Nucleic Acids Research 28, E47.
  • Speed, T. (2003). Statistical Analysis of Gene Expression Microarray Data. Chapman & Hall, New York.
  • 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 hibridization. Molecular Biology of the Cell 9, 32733297.
  • Van Der Laan, M. J. and Bryan, J. (2001). Gene expression analysis with parametric bootstrap. Biostatistics 2, 445461.
  • Vidakovic, B. (2006). Statistical Modelling by Wavelets. Wiley, New York.
  • Wang, H. Q. and Huang, D. S. (2005). A gene selection algorithm based on the gene regulation probability using maximal likelihood estimation. Biotechnology Letters 27, 597603.
  • Wu, P. S. and Müller, H. G. (2010). Functional embedding for the classification of gene expression profiles. Bioinformatics 26, 509517.
  • Wu, H. and Zhang, J. T. (2006). Nonparametric Regression Methods for Longitudinal Data Analysis. Wiley, New Jersey.
  • Yao, F., Müler, H. G., Clifford, A. J., Dueker, S. R., Follett, J., Lin, Y., Buchholz, B. A. and Vogel, J. S. (2003). Shrinkage estimation for functional principal component scores, with application to the population kinetics of plasma folate. Biometrics 59, 676685.
  • Zhao, X., Marron, J. S. and Wells, M. T. (2004). The functional data analysis view of longitudinal data. Statistica Sinica 14, 789808.