1 |
Cleveland, W. S. and Devlin, S .G. (1988). Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 83, 596-610
DOI
ScienceOn
|
2 |
Tran, P. H., Peiffer, D. A., Shin, Y., Meek, L. M., Brody, J. P., and Cho, K. W. (2002). Microarray optimizations: increasing spot accuracy and automated identification of true microarray signals. Nucleic Acids Res. 30, e54
DOI
PUBMED
ScienceOn
|
3 |
Tsodikov, A., Szabo, A., and Jones, D. (2002). Adjustments and measures of differential expression for microarray data. Bioinformatics 18, 251-60
DOI
ScienceOn
|
4 |
Dudoit, S., Fridlyand, J., and Speed., T.P.(2000). Comparison of Discrimination Methods for the Classification of Tumors Using Gene Experssion Data. Dept. of Statistics, University of California at Berkeley, Technical reports
|
5 |
David, J., Duggan, M. B, Yidong, C., Paul, M., and Jeffrey, M. T. (1999). Expression profiling using cDNA microarrays. Nature genetics supplement. 21, 10-14
DOI
ScienceOn
|
6 |
Michael, B., Eisen, P., and Brown, O. (2000). NA arrays for analysis of Gene Expression. Stanford University School of Medicine, Stanford, CA, Technical reports
|
7 |
Hedenfalk, L. et al. (2001). Gene-expression profiles in hereditary breast cancer. N. Engl. J. Med. 344, 539-548
DOI
ScienceOn
|
8 |
Wang, Y., Lu, J., Lee, R., Gu, Z., and Clarke, R. (2002). Iterative normalization of cDNA microarray data. IEEE Trans Inf. Technol. Biomed. 6, 29-37
DOI
ScienceOn
|
9 |
Yang, Y. H., Dudoit, S., Luu, P., Lin, D. M., Peng ,V., Ngai, J., and Speed, T. P. (2002). Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res. 30, e15
DOI
PUBMED
ScienceOn
|
10 |
Chen, Y., Dougherty, E. R., and Bittner, M. (1997). Ratiobased decisions and the quantitative analysis of cDNA microarray images. J. Biomed.Opt. 2,364-374
DOI
ScienceOn
|
11 |
Tseng, G. C., Oh, M. K., Rohlin, L., Liao, J. C., and Wong, W. H. (2001). Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res. 29, 2549-57
DOI
ScienceOn
|
12 |
Schuchhardt, J., Beule, D., Malik, A., Wolski, E., Eickhoff, H., Lehrach, H., and Herzel, H. (2000). Normalization strategies for cDNA microarrays. Nucleic Acids Res. 28(10)E47
DOI
PUBMED
|
13 |
Kim, J. H., Shin, D. M., and Lee, Y. S. (2002). Effect of local background intensities in thenormalization of cDNA microarray data with a skewed expression profiles. Exp MoI Med. 34, 224-232
DOI
ScienceOn
|
14 |
Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M .L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., and Lander, E. S. (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439), 531-7
DOI
PUBMED
ScienceOn
|
15 |
Zien, A., Aigner, T., Zimmer, R., and Lengauer, T. (2001). Centralization: a new method for the normalization of gene expression data. Bioinformatics 17(SuppI. 1), S323-31
DOI
PUBMED
|
16 |
Kepler, T. B., Crosby, L., and Morgan, K. T. (2002). Normalization and analysis of DNA microarray data by self-consistency and local regression. Genome Biol. 3(7), RESEARCH0037
|
17 |
Quackenbush, J. (2001). Computional Analysis of micorarray data. Nature 418-427
|
18 |
Dudoit, S., Yang, Y. H., Callow, M. J., and Speed., T. P. (2000). Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Dept. of Statistics, University of California at Berkeley, Technical reports
|