-
1
-
-
0033536012
-
Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays
-
Alon, U., Barkai, N., Notterman, D.A., Gish, K., Ybarra, S., Mack, D., and Levine, A.J. 1999. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 96(12), 6745-6750.
-
(1999)
Proc. Natl. Acad. Sci.
, vol.96
, Issue.12
, pp. 6745-6750
-
-
Alon, U.1
Barkai, N.2
Notterman, D.A.3
Gish, K.4
Ybarra, S.5
Mack, D.6
Levine, A.J.7
-
2
-
-
0037461021
-
Effective dimension reduction methods for tumor classification using gene expression data
-
Antoniadis, A., Lambert-Lacroix, S., and Leblanc, F. 2003. Effective dimension reduction methods for tumor classification using gene expression data. Bioinformatics 19, 563-570.
-
(2003)
Bioinformatics
, vol.19
, pp. 563-570
-
-
Antoniadis, A.1
Lambert-Lacroix, S.2
Leblanc, F.3
-
3
-
-
0034948896
-
A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inference of gene changes
-
Baldi, P., and Long, A.D. 2001. A Bayesian framework for the analysis of microarray expression data: Regularized t-test and statistical inference of gene changes. Bioinformatics 17(6), 509-519.
-
(2001)
Bioinformatics
, vol.17
, Issue.6
, pp. 509-519
-
-
Baldi, P.1
Long, A.D.2
-
4
-
-
0038238080
-
Statistical methods for ranking differentially expressed genes
-
Broberg, P. 2003. Statistical methods for ranking differentially expressed genes. Genome Biol. 4, R41.
-
(2003)
Genome Biol.
, vol.4
-
-
Broberg, P.1
-
5
-
-
0003594381
-
-
chapter 10, Duxbury, Belmont, CA
-
Casella, G., and Berger, R.L. 2002. Statistical Inference, 2nd ed., chapter 10, Duxbury, Belmont, CA.
-
(2002)
Statistical Inference, 2nd Ed.
-
-
Casella, G.1
Berger, R.L.2
-
6
-
-
0031246430
-
Ratio-based decisions and the quantitative analysis of cDNA microarray images
-
Chen, Y., Dougherty, E., and Bittner, M.L. 1997. Ratio-based decisions and the quantitative analysis of cDNA microarray images. J. Biomed. Optics 2, 364-374.
-
(1997)
J. Biomed. Optics
, vol.2
, pp. 364-374
-
-
Chen, Y.1
Dougherty, E.2
Bittner, M.L.3
-
7
-
-
0036128489
-
Dimension reduction strategies for analyzing global gene expression data with a response
-
Chiaromonte, F., and Martinelli, J. 2002. Dimension reduction strategies for analyzing global gene expression data with a response. Math. Biosci. 176(1), 123-144.
-
(2002)
Math. Biosci.
, vol.176
, Issue.1
, pp. 123-144
-
-
Chiaromonte, F.1
Martinelli, J.2
-
8
-
-
0032837252
-
Computational methods for the identification of differential and coordinated gene expression
-
Claverie, J.M. 1999. Computational methods for the identification of differential and coordinated gene expression. Human Mol. Genet. 8, 1821-1832.
-
(1999)
Human Mol. Genet.
, vol.8
, pp. 1821-1832
-
-
Claverie, J.M.1
-
10
-
-
0004184934
-
-
chapter 9, Chapman and Hall, London
-
Cox, D.R., and Hinkley, D.V. 1974. Theoretical Statistics, chapter 9, Chapman and Hall, London.
-
(1974)
Theoretical Statistics
-
-
Cox, D.R.1
Hinkley, D.V.2
-
11
-
-
0038494599
-
Unsupervised feature selection via two-way ordering in gene expression analysis
-
Ding, C.H.Q. 2003. Unsupervised feature selection via two-way ordering in gene expression analysis. Bioinformatics 19, 1259-1266.
-
(2003)
Bioinformatics
, vol.19
, pp. 1259-1266
-
-
Ding, C.H.Q.1
-
12
-
-
0036606753
-
Statistical intelligence: Effective analysis of high-density microarray data
-
Draghici, S. 2002. Statistical intelligence: Effective analysis of high-density microarray data. Drug Discovery Today 7(11), S55-S63.
-
(2002)
Drug Discovery Today
, vol.7
, Issue.11
-
-
Draghici, S.1
-
13
-
-
0036489046
-
Comparison of discrimination methods for the classification of tumors using gene expression data
-
Dudoit, S., Fridlyand, J., and Speed, T.P. 2002, Comparison of discrimination methods for the classification of tumors using gene expression data. J. American Statist. Assoc. 97(457), 77-87.
-
(2002)
J. American Statist. Assoc.
, vol.97
, Issue.457
, pp. 77-87
-
-
Dudoit, S.1
Fridlyand, J.2
Speed, T.P.3
-
14
-
-
0034863834
-
Classification of microarray data with penalized logistic regression
-
Eilers, P.H., Boer, J.M., van Ommen, G.J., and van Houwelingen, H.C. 2001. Classification of microarray data with penalized logistic regression. Proceedings of SPIE 4266, 187-198.
-
(2001)
Proceedings of SPIE
, vol.4266
, pp. 187-198
-
-
Eilers, P.H.1
Boer, J.M.2
Van Ommen, G.J.3
Van Houwelingen, H.C.4
-
15
-
-
2242436409
-
Geometric understanding of likelihood ratio statistics
-
Fan, J., Hung, H.N., and Wong, W.H. 2000. Geometric understanding of likelihood ratio statistics. J. Am. Statist. Assoc. 95, 836-841.
-
(2000)
J. Am. Statist. Assoc.
, vol.95
, pp. 836-841
-
-
Fan, J.1
Hung, H.N.2
Wong, W.H.3
-
17
-
-
0033569406
-
Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
-
Golub, T.R., Sonim, D.K., Tamayo, P., Huard, C., Gassenbeek, 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, 531-536.
-
(1999)
Science
, vol.286
, pp. 531-536
-
-
Golub, T.R.1
Sonim, D.K.2
Tamayo, P.3
Huard, C.4
Gassenbeek, M.5
Mesirov, J.P.6
Coller, H.7
Loh, M.L.8
Downing, J.R.9
Caligiuri, M.A.10
Bloomfield, C.D.11
Lander, E.S.12
-
18
-
-
0002718763
-
Using non-parametric methods in the context of multiple testing to determine differentially expressed genes
-
Kluwer Academic, NY
-
Grant, G., Manduchi, E., and Stoeckert Jr., C. 2002. Using non-parametric methods in the context of multiple testing to determine differentially expressed genes. In Methods of Microarray Data Analysis: Papers from CAMDA'00, 37-55, Kluwer Academic, NY
-
(2002)
Methods of Microarray Data Analysis: Papers from CAMDA'00
, pp. 37-55
-
-
Grant, G.1
Manduchi, E.2
Stoeckert Jr., C.3
-
19
-
-
0003397430
-
Statistical Theory of Extreme Values and Some Practical Applications
-
US Government Printing Office
-
Gumbel, E.J. 1954. Statistical Theory of Extreme Values and Some Practical Applications, National Bureau of Standards Applied Mathematics Series 33, US Government Printing Office.
-
(1954)
National Bureau of Standards Applied Mathematics Series 33
-
-
Gumbel, E.J.1
-
21
-
-
3142653304
-
Some applications of extreme-value methods
-
Gumbel, E.J., and Lieblein, J. 1954. Some applications of extreme-value methods. American Statistician 8(5), 14-17.
-
(1954)
American Statistician
, vol.8
, Issue.5
, pp. 14-17
-
-
Gumbel, E.J.1
Lieblein, J.2
-
22
-
-
84944979679
-
Order statistics from the gamma distribution
-
Gupta, S.S. 1960. Order statistics from the gamma distribution. Technometrics 2, 243-262.
-
(1960)
Technometrics
, vol.2
, pp. 243-262
-
-
Gupta, S.S.1
-
23
-
-
0003684449
-
-
Springer-Verlag, NY
-
Hastie, T., Tibshirani, R., and Friedman, J. 2001. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, NY
-
(2001)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
-
-
Hastie, T.1
Tibshirani, R.2
Friedman, J.3
-
24
-
-
0034921979
-
Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data
-
Ideker, T., Thorsson, V., Siegel, A.F., and Hood, L.E. 2000. Testing for differentially-expressed genes by maximum-likelihood analysis of microarray data. J. Comp. Biol. 7(6), 805-817.
-
(2000)
J. Comp. Biol.
, vol.7
, Issue.6
, pp. 805-817
-
-
Ideker, T.1
Thorsson, V.2
Siegel, A.F.3
Hood, L.E.4
-
25
-
-
0037976550
-
Improved gene selection for classification of microarrays
-
Jaeger, J., Sengupta, R., and Ruzzo, W.L. 2003. Improved gene selection for classification of microarrays. Pac. Symp. Biocomput. 8, 53-64.
-
(2003)
Pac. Symp. Biocomput.
, vol.8
, pp. 53-64
-
-
Jaeger, J.1
Sengupta, R.2
Ruzzo, W.L.3
-
26
-
-
12244265090
-
Gene selection: A Bayesian variable selection approach
-
Lee, K.E., Sha, N., Dougherty, E.R., Vannucci, M., and Mallick, B.K. 2003. Gene selection: A Bayesian variable selection approach. Bioinformatics 19, 90-97.
-
(2003)
Bioinformatics
, vol.19
, pp. 90-97
-
-
Lee, K.E.1
Sha, N.2
Dougherty, E.R.3
Vannucci, M.4
Mallick, B.K.5
-
27
-
-
18144444239
-
Cluster-Rasch models for microarray gene expression data
-
research 0031
-
Li, H., and Hong, F. 2001. Cluster-Rasch models for microarray gene expression data. Genome Biol. 2(8), research 0031.
-
(2001)
Genome Biol.
, vol.2
, Issue.8
-
-
Li, H.1
Hong, F.2
-
28
-
-
0037245821
-
Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients
-
Li, J., Liu, H., Downing, J.R., Yeoh, A.E.J., and Wong, L. 2003. Simple rules underlying gene expression profiles of more than six subtypes of acute lymphoblastic leukemia (ALL) patients. Bioinformatics 19, 71-78.
-
(2003)
Bioinformatics
, vol.19
, pp. 71-78
-
-
Li, J.1
Liu, H.2
Downing, J.R.3
Yeoh, A.E.J.4
Wong, L.5
-
29
-
-
0037543668
-
Computational analysis of leukemia microarray expression data using the GA/KNN method
-
Kluwer Academic, NY
-
Li, L., Pedersen, L.G., Darden, T.A., Weinberg, C.R. 2002. Computational analysis of leukemia microarray expression data using the GA/KNN method. In Methods of Microarray Data Analysis: Papers from CAMDA'00, 81-95, Kluwer Academic, NY.
-
(2002)
Methods of Microarray Data Analysis: Papers from CAMDA'00
, pp. 81-95
-
-
Li, L.1
Pedersen, L.G.2
Darden, T.A.3
Weinberg, C.R.4
-
30
-
-
0000984930
-
How many genes are needed for a discriminant microarray data analysis?
-
Kluwer Academic, NY
-
Li, W., and Yang, Y. 2002a. How many genes are needed for a discriminant microarray data analysis? In Methods of Microarray Data Analysis: Papers from CAMDA'00, 137-150, Kluwer Academic, NY.
-
(2002)
Methods of Microarray Data Analysis: Papers from CAMDA'00
, pp. 137-150
-
-
Li, W.1
Yang, Y.2
-
31
-
-
0036434526
-
Zipf's law in importance of genes for cancer classification using microarray data
-
Li, W., and Yang, Y. 2002b. Zipf's law in importance of genes for cancer classification using microarray data. J. Theoret. Biol. 219, 539-551.
-
(2002)
J. Theoret. Biol.
, vol.219
, pp. 539-551
-
-
Li, W.1
Yang, Y.2
-
33
-
-
0037543670
-
Classical statistical approaches to molecular classification of cancer from gene expression profiling
-
Kluwer Academic, NY
-
Lu, J., Hardy, S., Tao, W.L., Muse, S., Weir, B., and Spruill, S. 2002. Classical statistical approaches to molecular classification of cancer from gene expression profiling. In Methods of Microarray Data Analysis: Papers from CAMDA'00, 97-107, Kluwer Academic, NY
-
(2002)
Methods of Microarray Data Analysis: Papers from CAMDA'00
, pp. 97-107
-
-
Lu, J.1
Hardy, S.2
Tao, W.L.3
Muse, S.4
Weir, B.5
Spruill, S.6
-
34
-
-
0035224384
-
Feature selection for DNA methylation based cancer classification
-
Model, F., Adorjan, P., Olek, A., and Piepenbrock, C. 2001. Feature selection for DNA methylation based cancer classification. Bioinformatics 17(Suppl. 1), S157-S164.
-
(2001)
Bioinformatics
, vol.17
, Issue.1 SUPPL.
-
-
Model, F.1
Adorjan, P.2
Olek, A.3
Piepenbrock, C.4
-
35
-
-
0037590376
-
The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data
-
Mutch, D.M., Berger, A., Mansourian, R., Rytz, A., and Roberts, M.A. 2002. The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data. BMC Bioinformatics 3, 17.
-
(2002)
BMC Bioinformatics
, vol.3
, pp. 17
-
-
Mutch, D.M.1
Berger, A.2
Mansourian, R.3
Rytz, A.4
Roberts, M.A.5
-
36
-
-
0036166439
-
Tumor classification by partial squares using microarray gene expression data
-
Nguyen, D.V., and Rocke, D.M. 2002. Tumor classification by partial squares using microarray gene expression data. Bioinformatics 18, 39-50.
-
(2002)
Bioinformatics
, vol.18
, pp. 39-50
-
-
Nguyen, D.V.1
Rocke, D.M.2
-
37
-
-
0035999977
-
A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments
-
Pan, W. 2002. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18(4), 546-554.
-
(2002)
Bioinformatics
, vol.18
, Issue.4
, pp. 546-554
-
-
Pan, W.1
-
38
-
-
0041592555
-
On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression
-
Pan, W. 2003. On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression. Bioinformatics 19(11), 1333-1340.
-
(2003)
Bioinformatics
, vol.19
, Issue.11
, pp. 1333-1340
-
-
Pan, W.1
-
39
-
-
2142854576
-
A mixture model approach to detecting differentially expressed genes with microarray data
-
Pan, W., Lin, J., and Le, C. 2003. A mixture model approach to detecting differentially expressed genes with microarray data. Functional and Integrative Genomics 3, 117-124.
-
(2003)
Functional and Integrative Genomics
, vol.3
, pp. 117-124
-
-
Pan, W.1
Lin, J.2
Le, C.3
-
41
-
-
0037881525
-
Gene discovery in bladder cancer progression using cDNA microarrays
-
Sanchez-Carbayo, M., Socci, N., Lozano, J.J., Li, W., Belbin, T., Preystowski, M., Ortiz, A., Childs, G., and Cordon-Cardo, C. 2003. Gene discovery in bladder cancer progression using cDNA microarrays. Am. J. Path. 163, 505-516.
-
(2003)
Am. J. Path.
, vol.163
, pp. 505-516
-
-
Sanchez-Carbayo, M.1
Socci, N.2
Lozano, J.J.3
Li, W.4
Belbin, T.5
Preystowski, M.6
Ortiz, A.7
Childs, G.8
Cordon-Cardo, C.9
-
42
-
-
0038219199
-
A simple and efficient algorithm for gene selection using sparse logistic regression
-
Control Division, Department of Mechanical Engineering, National University of Singapore
-
Shevade, S.K., and Keerth, S.S. 2002. A simple and efficient algorithm for gene selection using sparse logistic regression. Technical Report CD-02-22, Control Division, Department of Mechanical Engineering, National University of Singapore.
-
(2002)
Technical Report CD-02-22
-
-
Shevade, S.K.1
Keerth, S.S.2
-
43
-
-
0035116995
-
Making sense of microarrays. (meeting report)
-
reports 4003
-
Siedow, J.N. 2001. Making sense of microarrays. (meeting report), Genome Biol. 2(2), reports 4003.
-
(2001)
Genome Biol.
, vol.2
, Issue.2
-
-
Siedow, J.N.1
-
44
-
-
0038048164
-
Statistical issues in cDNA microarray data analysis
-
Humana Press, Totowa, NJ
-
Smyth, G.K., Yang, Y.H., and Speed, T. 2003. Statistical issues in cDNA microarray data analysis. In Functional Genomics: Methods and Protocols, Methods in Molecular Biology Series, vol. 224, 111-136, Humana Press, Totowa, NJ.
-
(2003)
Functional Genomics: Methods and Protocols, Methods in Molecular Biology Series
, vol.224
, pp. 111-136
-
-
Smyth, G.K.1
Yang, Y.H.2
Speed, T.3
-
45
-
-
0004249246
-
-
Freeman, San Francisco
-
Sokal, R.R., and Rohlf, F.J. 1995. Biometry, 3rd ed., Freeman, San Francisco.
-
(1995)
Biometry, 3rd Ed.
-
-
Sokal, R.R.1
Rohlf, F.J.2
-
46
-
-
0003477556
-
-
chapter 22, Edward Arnold, London
-
Sturt, A., Ord, J.K., Arnold, S., and Kendall, M. 1999. Kendall's Advanced Theory of Statistics: Vol. 2A: Classical Inference and the Linear Model, 6th ed., chapter 22, Edward Arnold, London.
-
(1999)
Kendall's Advanced Theory of Statistics: Vol. 2A: Classical Inference and the Linear Model, 6th Ed.
, vol.2
-
-
Sturt, A.1
Ord, J.K.2
Arnold, S.3
Kendall, M.4
-
47
-
-
0034911875
-
An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles
-
Thomas, J.G., Olson, J.M., Tapscott, S.J., and Zhao, L.P. 2001. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Res. 11, 1227-1236.
-
(2001)
Genome Res.
, vol.11
, pp. 1227-1236
-
-
Thomas, J.G.1
Olson, J.M.2
Tapscott, S.J.3
Zhao, L.P.4
-
48
-
-
2342533421
-
Class prediction by nearest shrunken centroids, with applications to DNA microarrays
-
Tibshitani, R., Hastie, T., Narasimhan, B., and Chu, G. 2003. Class prediction by nearest shrunken centroids, with applications to DNA microarrays. Statist. Sci. 18, 104-117.
-
(2003)
Statist. Sci.
, vol.18
, pp. 104-117
-
-
Tibshitani, R.1
Hastie, T.2
Narasimhan, B.3
Chu, G.4
-
49
-
-
18244409687
-
Gene expression profiling predicts clinical outcome of breast cancer
-
Van't Veer, L.J., Dai, H., et al. 2002. Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530-536.
-
(2002)
Nature
, vol.415
, pp. 530-536
-
-
Van't Veer, L.J.1
Dai, H.2
-
50
-
-
0001972601
-
The large sample distribution of the likelihood ratio for testing composite hypothesis
-
Wilks, S.S. 1938. The large sample distribution of the likelihood ratio for testing composite hypothesis. Ann. Math. Statist. 9, 60-62.
-
(1938)
Ann. Math. Statist.
, vol.9
, pp. 60-62
-
-
Wilks, S.S.1
-
52
-
-
0034894874
-
Feature (gene) selection in gene expression-based tumor classification
-
Xiong, M., Li, W.J., Zhao, J., Jin, L., and Boerwinkle, E. 2001. Feature (gene) selection in gene expression-based tumor classification. Mol. Genet. Metabolism 73(3), 239-247.
-
(2001)
Mol. Genet. Metabolism
, vol.73
, Issue.3
, pp. 239-247
-
-
Xiong, M.1
Li, W.J.2
Zhao, J.3
Jin, L.4
Boerwinkle, E.5
|