References for Module 10

1. Schena, M., et al., Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 1995. 270(5235): p. 467-70.

2. Lipshutz, R.J., et al., Using oligonucleotide probe arrays to access genetic diversity. Biotechniques, 1995. 19(3): p. 442-7.

3. Chu, S., et al., The transcriptional program of sporulation in budding yeast. Science, 1998. 282(5389): p. 699-705.

4. Wodicka, L., et al., Genome-wide expression monitoring in Saccharomyces cerevisiae. Nat Biotechnol, 1997. 15(13): p. 1359-67.

5. Lockhart, D.J., et al., Expression monitoring by hybridization to high-density oligonucleotide arrays. Nat Biotechnol, 1996. 14(13): p. 1675-80.

6. Baltimore, D., Our Genome Unveiled. Nature, 2001. 409: p. 814-816.

7. Velculescu, V.E., et al., Serial analysis of gene expression. Science, 1995. 270(5235): p. 484-7.

8. Liang, P. and A.B. Pardee, Differential display of eukaryotic messenger RNA by means of the polymerase chain reaction. Science, 1992. 257(5072): p. 967-71.

9. Crick, F., Central dogma of molecular biology. Nature, 1970. 227(258): p. 561-3.

10. Velculescu, V.E., et al., Characterization of the yeast transcriptome. Cell, 1997. 88(2): p. 243-51.

11. Churchill, G.A., Fundamentals of experimental design for cDNA microarrays. Nat Genet, 2002. 32 Suppl: p. 490-5.

12. Barrett, J.C. and E.S. Kawasaki, Microarrays: the use of oligonucleotides and cDNA for the analysis of gene expression. Drug Discov Today, 2003. 8(3): p. 134-41.

13. Li, C. and W.H. Wong, Model-based analysis of oligonucleotide arrays: model validation, design issues, and standard error application. Genome Biology, 2001. 2(8): p. 32.1 - 32.11.

14. Hoffmann, R., T. Seidl, and M. Dugas, Profound effect of normalization on detection of differentially expressed genes in oligonucleotide microarray data analysis. Genome Biol, 2002. 3(7): p. RESEARCH0033.

15. Hautaniemi, S., et al., A novel strategy for microarray quality control using Bayesian networks. Bioinformatics, 2003. 19(16): p. 2031-8.

16. Smyth, G.K., Y.H. Yang, and T. Speed, Statistical issues in cDNA microarray data analysis. Methods Mol Biol, 2003. 224: p. 111-36.

17. Tseng, G.C., et al., Issues in cDNA microarray analysis: quality filtering, channel normalization, models of variations and assessment of gene effects. Nucleic Acids Res, 2001. 29(12): p. 2549-57.

18. Jenssen, T.K., et al., Analysis of repeatability in spotted cDNA microarrays. Nucleic Acids Res, 2002. 30(14): p. 3235-44.

19. Gollub, J., et al., The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Res, 2003. 31(1): p. 94-6.

20. Quackenbush, J., Microarray data normalization and transformation. Nat Genet, 2002. 32 Suppl: p. 496-501.

21. Yang, Y.H., et al., Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res, 2002. 30(4): p. e15.

22. Workman, C., et al., A new non-linear normalization method for reducing variability in DNA microarray experiments. Genome Biol, 2002. 3(9): p. research0048.

23. Eisen, M.B., et al., Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A, 1998. 95(25): p. 14863-8.

24. Golub, T.R., et al., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 1999. 286(5439): p. 531-7.

25. Zhong, S., C. Li, and W.H. Wong, ChipInfo: Software for extracting gene annotation and gene ontology information for microarray analysis. Nucleic Acids Res, 2003. 31(13): p. 3483-6.

26. Jenssen, T.K., et al., A literature network of human genes for high-throughput analysis of gene expression. Nat Genet, 2001. 28(1): p. 21-8.

27. Mootha, V.K., et al., PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet, 2003. 34(3): p. 267-73.

28. Butte, A.J., et al., Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci U S A, 2000. 97(22): p. 12182-6.

29. Nevins, J.R., et al., Towards Integrated Clinico-Genomic Models for Personalized Medicine: Combining Gene Expression Signatures and Clinical Factors in Breast Cancer Outcomes Prediction. Hum Mol Genet, 2003.

30. Chuaqui, R.F., et al., Post-analysis follow-up and validation of microarray experiments. Nat Genet, 2002. 32 Suppl: p. 509-14.

31. Slonim, D.K., From patterns to pathways: gene expression data analysis comes of age. Nat Genet, 2002. 32 Suppl: p. 502-8.

32. Dougherty, E., Small sample issues for microarray-based classification. Comparative and Functional Genomics, 2001. 2: p. 28-34.

33. Kerr, M.K. and G.A. Churchill, Bootstrapping cluster analysis: assessing the reliability of conclusions from microarray experiments. Proc Natl Acad Sci U S A, 2001. 98(16): p. 8961-5.

34. Azuaje, F., Genomic data sampling and its effect on classification performance assessment. BMC Bioinformatics, 2003. 4(1): p. 5.

35. Singh, D., et al., Gene expression correlates of clinical prostate cancer behavior. Cancer Cell, 2002. 1(2): p. 203-9.

36. Dyrskjot, L., et al., Identifying distinct classes of bladder carcinoma using microarrays. Nat Genet, 2003. 33(1): p. 90-6.

37. Nutt, C.L., et al., Gene expression-based classification of malignant gliomas correlates better with survival than histological classification. Cancer Res, 2003. 63(7): p. 1602-7.

38. Pomeroy, S.L., et al., Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature, 2002. 415(6870): p. 436-42.

39. Shipp, M.A., et al., Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Nat Med, 2002. 8(1): p. 68-74.

40. Stickney, H.L., et al., Rapid mapping of zebrafish mutations with SNPs and oligonucleotide microarrays. Genome Res, 2002. 12(12): p. 1929-34.

41. Pollack, J.R., et al., Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors. Proc Natl Acad Sci U S A, 2002. 99(20): p. 12963-8.

42. Brazma, A., et al., Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet, 2001. 29(4): p. 365-71.

43. Spellman, P.T., et al., Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell, 1998. 9(12): p. 3273-97.

44. Iyer, V.R., et al., The transcriptional program in the response of human fibroblasts to serum. Science, 1999. 283(5398): p. 83-7.

45. Le Naour, F., et al., Profiling changes in gene expression during differentiation and maturation of monocyte-derived dendritic cells using both oligonucleotide microarrays and proteomics. J Biol Chem, 2001. 276(21): p. 17920-31.

46. Quackenbush, J., Genomics. Microarrays--guilt by association. Science, 2003. 302(5643): p. 240-1.

47. Kim, S.K., et al., A gene expression map for Caenorhabditis elegans. Science, 2001. 293(5537): p. 2087-92.

48. Walhout, A.J., et al., Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline. Curr Biol, 2002. 12(22): p. 1952-8.

49. White, K.P., et al., Microarray analysis of Drosophila development during metamorphosis. Science, 1999. 286(5447): p. 2179-84.

50. Jin, W., et al., The contributions of sex, genotype and age to transcriptional variance in Drosophila melanogaster. Nat Genet, 2001. 29(4): p. 389-95.

51. Arbeitman, M.N., et al., Gene expression during the life cycle of Drosophila melanogaster. Science, 2002. 297(5590): p. 2270-5.

52. Ton, C., et al., Construction of a zebrafish cDNA microarray: gene expression profiling of the zebrafish during development. Biochem Biophys Res Commun, 2002. 296(5): p. 1134-42.

53. Stuart, J.M., et al., A gene-coexpression network for global discovery of conserved genetic modules. Science, 2003. 302(5643): p. 249-55.

54. McCarroll, S.A., et al., Comparing genomic expression patterns across species identifies shared transcriptional profile in aging. Nat Genet, 2004. 36(2): p. 197-204.

55. Hughes, T.R., et al., Functional discovery via a compendium of expression profiles. Cell, 2000. 102(1): p. 109-26.

56. Schadt, E.E., S.A. Monks, and S.H. Friend, A new paradigm for drug discovery: integrating clinical, genetic, genomic and molecular phenotype data to identify drug targets. Biochem Soc Trans, 2003. 31(2): p. 437-43.

57. Stegmaier, K., et al., Gene expression-based high-throughput screening(GE-HTS) and application to leukemia differentiation. Nat Genet, 2004. 36(3): p. 257-63.

58. Yeoh, E.J., et al., Classification, subtype discovery, and prediction of outcome in pediatric acute lymphoblastic leukemia by gene expression profiling. Cancer Cell, 2002. 1(2): p. 133-43.

59. Ramaswamy, S., et al., Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci U S A, 2001. 98(26): p. 15149-54.

60. Chan, W.C. and J.Z. Huang, Gene expression analysis in aggressive NHL. Ann Hematol, 2001. 80 Suppl 3: p. B38-41.

61. Alizadeh, A.A., et al., Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature, 2000. 403(6769): p. 503-11.

62. Ono, K., et al., Identification by cDNA microarray of genes involved in ovarian carcinogenesis. Cancer Res, 2000. 60(18): p. 5007-11.

63. Sallinen, S.L., et al., Identification of differentially expressed genes in human gliomas by DNA microarray and tissue chip techniques. Cancer Res, 2000. 60(23): p. 6617-22.

64. Lu, J., et al., Gene expression profile changes in initiation and progression of squamous cell carcinoma of esophagus. Int J Cancer, 2001. 91(3): p. 288-94.

65. Mori, M., et al., Analysis of the gene-expression profile regarding the progression of human gastric carcinoma. Surgery, 2002. 131(1 Suppl): p. S39-47.

66. Jiang, Y., et al., Discovery of differentially expressed genes in human breast cancer using subtracted cDNA libraries and cDNA microarrays. Oncogene, 2002. 21(14): p. 2270-82.

67. Rubin, M.A., et al., alpha-Methylacyl coenzyme A racemase as a tissue biomarker for prostate cancer. Jama, 2002. 287(13): p. 1662-70.

68. Luo, J., et al., Alpha-methylacyl-CoA racemase: a new molecular marker for prostate cancer. Cancer Res, 2002. 62(8): p. 2220-6.

69. Hedenfalk, I., et al., Gene-expression profiles in hereditary breast cancer. N Engl J Med, 2001. 344(8): p. 539-48.

70. Garber, M.E., et al., Diversity of gene expression in adenocarcinoma of the lung. Proc Natl Acad Sci U S A, 2001. 98(24): p. 13784-9.

71. Bhattacharjee, A., et al., Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc Natl Acad Sci U S A, 2001. 98(24): p. 13790-5.

72. Armstrong, S.A., et al., MLL translocations specify a distinct gene expression profile that distinguishes a unique leukemia. Nat Genet, 2002. 30(1): p. 41-7.

73. Yussman, M.G., et al., Mitochondrial death protein Nix is induced in cardiac hypertrophy and triggers apoptotic cardiomyopathy. Nat Med, 2002. 8(7): p. 725-30.

74. Heller, R.A., et al., Discovery and analysis of inflammatory disease-related genes using cDNA microarrays. Proc Natl Acad Sci U S A, 1997. 94(6): p. 2150-5.

75. Baelde, H.J., et al., Gene expression profiling in glomeruli from human kidneys with diabetic nephropathy. Am J Kidney Dis, 2004. 43(4): p. 636-50.

76. Whitney, L.W., et al., Analysis of gene expression in mutiple sclerosis lesions using cDNA microarrays. Ann Neurol, 1999. 46(3): p. 425-8.

77. Chabas, D., et al., The influence of the proinflammatory cytokine, osteopontin, on autoimmune demyelinating disease. Science, 2001. 294(5547): p. 1731-5.

78. van de Vijver, M.J., et al., A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med, 2002. 347(25): p. 1999-2009.

79. West, M., et al., Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci U S A, 2001. 98(20): p. 11462-7.

80. Huang, E., et al., Gene expression predictors of breast cancer outcomes. Lancet, 2003. 361(9369): p. 1590-6.

81. van 't Veer, L.J., et al., Gene expression profiling predicts clinical outcome of breast cancer. Nature, 2002. 415(6871): p. 530-6.

82. Henshall, S.M., et al., Survival analysis of genome-wide gene expression profiles of prostate cancers identifies new prognostic targets of disease relapse. Cancer Res, 2003. 63(14): p. 4196-203.

83. Li, S., et al., Comparative genome-scale analysis of gene expression profiles in T cell lymphoma cells during malignant progression using a complementary DNA microarray. Am J Pathol, 2001. 158(4): p. 1231-7.

84. Wright, G., et al., A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proc Natl Acad Sci U S A, 2003. 100(17): p. 9991-6.

85. Ramaswamy, S., et al., A molecular signature of metastasis in primary solid tumors. Nat Genet, 2003. 33(1): p. 49-54.

86. Blaxall, B.C., et al., Differential myocardial gene expression in the development and rescue of murine heart failure. Physiol Genomics, 2003. 15(2): p. 105-14.

87. Ueno, S., et al., DNA microarray analysis of in vivo progression mechanism of heart failure. Biochem Biophys Res Commun, 2003. 307(4): p. 771-7.

88. Youdim, M.B., et al., Early and late molecular events in neurodegeneration and neuroprotection in Parkinson's disease MPTP model as assessed by cDNA microarray; the role of iron. Neurotox Res, 2002. 4(7-8): p. 679-689.

89. Eikmans, M., et al., RNA expression profiling as prognostic tool in renal patients: toward nephrogenomics. Kidney Int, 2002. 62(4): p. 1125-35.

90. Leikauf, G.D., et al., Acute lung injury: functional genomics and genetic susceptibility. Chest, 2002. 121(3 Suppl): p. 70S-75S.

91. Chang, J.C., et al., Gene expression profiling for the prediction of therapeutic response to docetaxel in patients with breast cancer. Lancet, 2003. 362(9381): p. 362-9.

92. Faneyte, I.F., et al., Breast cancer response to neoadjuvant chemotherapy: predictive markers and relation with outcome. Br J Cancer, 2003. 88(3): p. 406-12.

93. Armstrong, S.A., T.R. Golub, and S.J. Korsmeyer, MLL-rearranged leukemias: insights from gene expression profiling. Semin Hematol, 2003. 40(4): p. 268-73.