1 |
Slonim, D. (2002). From patterns to pathways: Gene Expression data analysis come of age, Nature Genetics, 32, 502-508.
DOI
ScienceOn
|
2 |
Storey, J. D., Xiao, W., Leek, J., Tomkins, R. G. and Davis, R. W. (2005). Significance analysis of time course microarray experiments, Preceedings of the National Academy of Sciences, 102, 12837-12842.
DOI
ScienceOn
|
3 |
Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. S. and Goulb, T. R. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentitation, Proceedings of the National Academy of Sciences of the United States of America, 96, 2907-2912.
DOI
ScienceOn
|
4 |
Tavazoie, S., Huges, J. D., Campbell, M. J., Cho, R. J. and Church, G. M. (1999). Systematic determination of genetic network architecture, Nature Genetics, 22, 281-285.
DOI
ScienceOn
|
5 |
Wang, L., Chen, X., Wolfinger, R. D., Franklin, J. L., Coffey, R. J. and Zhang, B. (2009). A unified mixed effects model for gene set analysis of time course microarray experiments, Statistical Applications in Genetics and Molecular Biology, 8, Article 47.
|
6 |
Yeung, K. Y., Fraley, C., Murua, A., Raftery, A. E. and Ruzzo, W. L. (2001). Model-based clustering and data transformations for gene expression data, Bioinformatics, 17, 977-987.
DOI
ScienceOn
|
7 |
Yeung, K. Y., Medvedovic, M. and Bumgarner, R. E. (2003). Clustering gene-expression data with repeated measurements, Genome Biology, 4, R34.
DOI
|
8 |
Iyer, V. R., Eisen, M. B., Ross, D. T., Schuler, G., Moore, T., Lee, J. C., Trent, J. M., Staudt, L., Hudson, J., Boguski, M., Lashkari, D., Shalon, D., Botstein, D. and Brown, P. O. (1999). The transcriptional program in the response of human fibroblasts to serum, Science, 283, 83-87.
DOI
ScienceOn
|
9 |
Jordan, M. I. and Jacobs, R. A. (1992). Hierarchies of adaptive experts, Advances in Neural Information Processing Systems, 4, 985-993.
|
10 |
Jordan, M. I. and Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm, Neural Computation, 6, 181-214.
DOI
ScienceOn
|
11 |
Little, R. J. and Rubin, D. B. (2002). Statistical Analysis with Missing Data, Wiley.
|
12 |
Kerr, M. K. and Churchill G. A. (2001). Experimental design for gene expression microarrays, Biostatistics, 2, 183-201.
DOI
ScienceOn
|
13 |
Laird, N. M. and Ware, J. H. (1982). Random effect models for longitudinal data, Biometrics, 38, 963-974.
DOI
ScienceOn
|
14 |
Lander, E. S. (1999). Array of hope, Nature Genetics, 21, 3-4.
DOI
ScienceOn
|
15 |
Luan, Y. and Li, H. (2003). Clustering of time-course gene expression data using a mixed-effects model with B-splines, Bioinformatics, 19, 474-482.
DOI
ScienceOn
|
16 |
McCullagh, P. and Nelder, J. A. (1983). Generalized Linear Models, Chapman & Hall, London.
|
17 |
McLachlan, G. J. (2008). The EM Algorithm and Extensions, Wiley.
|
18 |
Pinheiro, J. and Bates, D. (2009). Mixed-Effects Models in S and S-PLUS 2nd Ed., Springer.
|
19 |
Quackenbush, J. (2001). Computational analysis of cDNA microarray data, Nature Review Genetics, 6, 418-428.
|
20 |
Schlattmann, P. (2009). Medical Applications of Finite Mixture Models, Springer.
|
21 |
Bridle J. (1989). Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Reconition, In Neurocomputing: Algorithms, Architectures, and Applications, Springer
|
22 |
Draghici, S. (2003). Data Analysis Tools for DNA Microarrays, Chapman & Hall.
|
23 |
Brown, M., Grundy, W., Lin, D., Cristianini, N., Sugnet, C., Furey, T., Ares, M. and Haussler, D. (2000). Knowledge-based analysis of microarray gene expression data by using support vector machines, Proceedings of the National Academy of Sciences of the United States of America, 97, 262-267.
DOI
ScienceOn
|
24 |
Costa, I. G., Carvalho, F. and Souto, M. (2004). Comparative analysis of clustering methods for gene expression time course data, Genetics and Molecular Biology, 27, 623-631.
DOI
ScienceOn
|
25 |
Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm, Journal of Royal Statistical Society Series B, 39, 1-38.
|
26 |
Eisen, M. B., Spellman, P. T., Brown, P. O. and Botstein, D. (1998). Cluster analysis and display of genomewide expression patterns, Proceedings of the National Academy of Sciences of the United States of America, 95, 14863-14868.
DOI
ScienceOn
|
27 |
Hartuv, E., Schmitt, A., Lange, J., Meirer-Ewert, S., Lehrach, H. and Shamir, R. (1999). An algorithm for clustering cDNAs for gene expression analysis, IN RECOMB99: Proceedings of the Third Annual International Conference on Computational Molecular Biology, Lyon, France, 188-197.
|
28 |
Hastie, T., Tibshirani, R. and Friedman, J. (2001). The Elements of Statistical Learning, Springer.
|