9. ΠΠ«ΠΠΠΠ«.
1. Π ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ΅ Π°Π½Π°Π»ΠΈΠ·Π° Π΄Π°Π½Π½ΡΡ
ΠΏΠΎ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΠΈ Π³Π΅Π½ΠΎΠ² Π² 31 ΠΎΠ±ΡΠ°Π·ΡΠ΅ ΡΠΊΠ°Π½ΠΈ, Π±ΡΠ» Π²ΡΠ΄Π΅Π»Π΅Π½ ΡΠΏΠΈΡΠΎΠΊ ΠΊΠΎΠ½ΡΡΠΈΡΡΡΠΈΠ²Π½ΡΡ
Π³Π΅Π½ΠΎΠ² ΡΠ°Π·ΠΌΠ΅ΡΠΎΠΌ 2374 Π³Π΅Π½Π°, ΠΈΠ· ΠΊΠΎΡΠΎΡΡΡ
1491 Π³Π΅Π½ΠΎΠ² ΡΠ°Π½Π΅Π΅ Π½Π΅ ΠΊΠ»Π°ΡΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π»ΠΈΡΡ ΠΊΠ°ΠΊ ΠΊΠΎΠ½ΡΡΠΈΡΡΡΠΈΠ²Π½ΡΠ΅. ΠΠΎΠΌΠΈΠΌΠΎ ΡΡΠΎΠ³ΠΎ Π΄Π»Ρ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΡΠΊΠ°Π½ΠΈ Π±ΡΠ» ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ ΡΠΊΠ°Π½Π΅ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΡΡ
Π³Π΅Π½ΠΎΠ², ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ½ΠΈΠΊΠ°Π»ΡΠ½ΠΎ ΡΠΊΡΠΏΡΠ΅ΡΡΠΈΡΡΡΡΡΡ Π² Π½Π΅ΠΉ, ΡΠ°Π·ΠΌΠ΅ΡΡ ΡΠΏΠΈΡΠΊΠΎΠ² Π²Π°ΡΡΠΈΡΡΡΡ ΠΎΡ 4 Π΄ΠΎ 484 Π³Π΅Π½ΠΎΠ².
2. ΠΡΠΏΠΎΠ»ΡΠ·ΡΡ Π΄Π°Π½Π½ΡΠ΅ Ρ ΠΠ«Π -ΡΠΈΠΏΠΎΠ² Π΄Π»Ρ 191 ΠΎΠ±ΡΠ°Π·ΡΠ° ΡΠ°ΠΊΠ° ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ, ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Ρ 30 Π°ΠΌΠΏΠ»ΠΈΠΊΠΎΠ½ΠΎΠ², 23 ΠΈΠ· ΠΊΠΎΡΠΎΡΡΡ
ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π΅Π½Ρ Π»ΠΈΡΠ΅ΡΠ°ΡΡΡΠ½ΡΠΌΠΈ Π΄Π°Π½Π½ΡΠΌΠΈ. ΠΡΠΈ ΡΡΠΎΠΌ Π±ΡΠ»ΠΎ ΠΈΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½ΠΎ 7 Π½ΠΎΠ²ΡΡ
Π°ΠΌΠΏΠ»ΠΈΠΊΠΎΠ½ΠΎΠ², ΡΠ°Π½Π΅Π΅ Π½Π΅ΠΈΠ·Π²Π΅ΡΡΠ½ΡΡ
— 5Ρ15, 7Ρ22, 7Ρ15, q22, 14Π΄22, 19Ρ13 ΠΈ 22Ρ13. ΠΠ°Π½Π½ΡΠ΅ Π°ΠΌΠΏΠ»ΠΈΠΊΠΎΠ½Ρ Π½ΡΠΆΠ΄Π°ΡΡΡΡ Π² Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅ΠΌ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΈ, ΡΠ°ΠΊ ΠΊΠ°ΠΊ ΠΎΠ½ΠΈ ΠΌΠΎΠ³ΡΡ ΠΈΠΌΠ΅ΡΡ ΠΏΡΡΠΌΠΎΠ΅ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠ΅ ΠΊ ΡΠ°ΠΊΡ ΠΈ ΡΠΎΠ΄Π΅ΡΠΆΠ°ΡΡ Π³Π΅Π½Ρ, ΡΠ°Π½Π΅Π΅ Π½Π΅ Π°ΡΡΠΎΡΠΈΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Ρ ΡΠ°ΠΊΠΎΠΌ ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ. ΠΠ΄Π΅Π½ΡΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ Π°ΠΌΠΏΠ»ΠΈΠΊΠΎΠ½Ρ ΡΠΎΡΡΠ°Π²Π»ΡΡΡ Π°ΠΌΠΏΠ»ΠΈΠΊΠΎΠΌ ΡΠ°ΠΊΠ° ΠΌΠΎΠ»ΠΎΡΠ½ΠΎΠΉ ΠΆΠ΅Π»Π΅Π·Ρ ΡΠ°Π·ΠΌΠ΅ΡΠΎΠΌ 1747 Π³Π΅Π½ΠΎΠ².
3. Π‘Π²ΠΎΠΉΡΡΠ²Π° ΡΠ΅ΡΠ΅ΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΠ±ΡΠ°Π·ΡΡΡ ΡΠΊΠ°Π½Π΅ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΡΠ΅ Π³Π΅Π½Ρ, ΡΠΎΠ³Π»Π°ΡΡΡΡΡΡ Ρ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΠΎΠ½ΡΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΈ ΠΎΡΡΠ°ΠΆΠ°ΡΡ Π² ΠΏΠΎΠ»Π½ΠΎΠΉ ΠΌΠ΅ΡΠ΅ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΡΡ ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΡ ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΡΠΊΠ°Π½ΠΈ.
4. Π‘ΠΎΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΡΡΠ°ΡΠΈΠΈ ΠΈ Π°ΠΌΠΏΠ»ΠΈΡΠΈΠΊΠ°ΡΠΈΠΈ ΠΎΠ±ΡΠ°Π·ΡΡΡ ΡΠ΅ΡΠΈ, ΠΏΠΎ ΡΠΎΠΏΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΊΠΎΡΠΎΡΡΡ
ΠΌΠΎΠΆΠ½ΠΎ ΡΠΊΠ°Π·Π°ΡΡ, ΡΡΠΎ ΠΎΠ±Π° ΡΠΏΠΈΡΠΊΠ° ΡΠ²Π»ΡΡΡΡΡ ΡΠΈΠ»ΡΠ½ΠΎ ΡΠ²ΡΠ·Π°Π½Π½ΡΠΌΠΈ Π²Π½ΡΡΡΠΈ ΡΠ΅Π±Ρ, ΠΏΡΠΈΡΠ΅ΠΌ Π΄Π»Ρ ΠΌΡΡΠΎΠΌΠ° Π±ΡΠ»Π° ΠΏΠΎΠ»ΡΡΠ΅Π½Π° Π±ΠΎΠ»ΡΡΠ°Ρ ΡΡΠ΅ΠΏΠ΅Π½Ρ ΡΠ²ΡΠ·Π°Π½Π½ΠΎΡΡΠΈ ΠΏΠΎ Π²Ρ
ΠΎΠ΄ΡΡΠΈΠΌ ΡΠ²ΡΠ·ΡΠΌ. ΠΡΡΠΎΠΌ ΡΠ°ΠΊΠΆΠ΅ ΠΏΡΠΎΠ΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΠΎΠ²Π°Π» Π±ΠΎΠ»ΡΡΠ΅Π΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΠ²ΡΠ·Π΅ΠΉ ΡΠΈΠΏΠ° «ΡΠ΅Π³ΡΠ»ΡΡΠΈΡ ΡΡΠ°Π½ΡΠΊΡΠΈΠΏΡΠΈΠΈ» ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ Π°ΠΌΠΏΠ»ΠΈΠΊΠΎΠ½Π°ΠΌ, ΡΠ΅ΠΌ Π°ΠΌΠΏΠ»ΠΈΠΊΠΎΠ½Ρ ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ ΠΌΡΡΠΎΠΌΡ. ΠΡΠΎ ΠΏΠΎΠ΄ΡΠ²Π΅ΡΠΆΠ΄Π°Π΅Ρ ΡΠ΅Π³ΡΠ»ΡΡΠΎΡΠ½ΡΡ ΡΠΎΠ»Ρ ΠΌΡΡΠΎΠΌΠ° ΠΏΠΎ ΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΡ ΠΊ Π°ΠΌΠΏΠ»ΠΈΠΊΠΎΠΌΡ.
5. Π Π°Π·Π»ΠΈΡΠ½ΡΠ΅ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠΏΠΎΡΠΎΠ±Π½Ρ Π³Π΅Π½Π΅ΡΠΈΡΠΎΠ²Π°ΡΡ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΡ, ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠΏΠΎΡΠΎΠ±Π½Ρ ΠΎΠ±ΡΠ°Π·ΠΎΠ²ΡΠ²Π°ΡΡ ΡΠ΅ΡΠΈ, ΡΠΎ ΡΡ
ΠΎΠΆΠΈΠΌΠΈ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠ°ΠΌΠΈ, Π½ΠΎ ΠΏΡΠΈ ΡΡΠΎΠΌ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΡΠ΅ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΡ Π½Π΅ ΡΠ²Π»ΡΡΡΡΡ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΌΠΈ Π΅Π΄ΠΈΠ½ΠΈΡΠ°ΠΌΠΈ. ΠΡΠΈ ΡΡΠΎΠΌ Π² ΡΠΎΡΡΠ°Π² ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠΎΠ² ΠΈΠΌΠ΅ΡΡ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΡ ΠΏΠΎΠΏΠ°Π΄Π°ΡΡ ΠΏΡΠ΅ΠΈΠΌΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎ Π³Π΅Π½Ρ-ΠΌΠΈΡΠ΅Π½ΠΈ ΡΡΠ°Π½ΡΠΊΡΠΈΠΏΡΠΈΠΎΠ½Π½ΡΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ².
6. ΠΠ½ΡΠ΅ΡΠ°ΠΊΡΠΎΠΌΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠ»Π΅Π·Π½ΡΠΌ ΠΈΠ½ΡΡΡΡΠΌΠ΅Π½ΡΠΎΠΌ Π°Π½Π°Π»ΠΈΠ·Π° ΠΊΡΡΠΏΠ½ΠΎΠΌΠ°ΡΡΡΠ°Π±Π½ΡΡ
Π΄Π°Π½Π½ΡΡ
, ΠΊΠΎΡΠΎΡΡΠΉ ΠΎΡΠ»ΠΈΡΠ½ΠΎ Π΄ΠΎΠΏΠΎΠ»Π½ΡΠ΅Ρ ΡΡΠ°Π²ΡΠΈΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΌ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΉ Π°Π½Π°Π»ΠΈΠ· ΠΎΠ½ΡΠΎΠ»ΠΎΠ³ΠΈΠΉ Π±ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΏΡΠΎΡΠ΅ΡΡΠΎΠ². ΠΠΎ ΠΏΡΠΈ ΡΡΠΎΠΌ ΡΠ²Π»ΡΠ΅ΡΡΡ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎ ΠΈΠ½ΡΠΎΡΠΌΠ°ΡΠΈΠ²Π½ΡΠΌ, Π΅ΡΠ»ΠΈ Π΅Π³ΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎ. ΠΠ΅ΡΠΎΠΌΠ½Π΅Π½Π½ΠΎ, Π΄Π°Π½Π½ΡΠΉ Π²ΠΈΠ΄ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π½ΡΠΌ Π΄Π»Ρ ΡΠ°Π·Π²ΠΈΡΠΈΡ Π² Π±ΡΠ΄ΡΡΠ΅ΠΌ.
1. Ardrey, R.E. and R. Ardrey, Liquid chromatography-mass spectrometiy: an introduction. 2003, London: J. Wiley.2. de Hoffmann, E. and V. Stroobant, Mass spectrometry: Principles and applications. 2001: John Wiley & Sons.
2. Patterson, S.D. and R.H. Aebersold, Proteomics: the first decade and beyond. Nat Genet, 2003. 33 Suppl: p. 311−23.
3. Carter, N.P., Methods and strategies for analyzing copy number variation using DNA microarrays. Nat Genet, 2007. 39(7 Suppl): p. SI6−21.
4. Wood, L.D., et al., The genomic landscapes of human breast and colorectal cancers. Science, 2007. 318(5853): p. 1108−13.
5. Jones, S., et al., Core signaling pathways in human pancreatic cancers revealed by global genomic analyses. Science, 2008. 321(5897): p. 1801−6.
6. Parsons, D.W., et al., An integrated genomic analysis of human glioblastoma multiforme. Science, 2008. 321(5897): p. 1807−12.
7. Li, H. and W. Wang, Dissecting the transcription networks of a cell using computational genomics. Curr Opin Genet Dev, 2003. 13(6): p. 611.-6.
8. Wang, W., et al., Inference of combinatorial regulation in yeast transcriptional networks: a case study of sporulation. Proc Natl Acad Sci USA, 2005. 102(6): p. 1998;2003.
9. Bar-Joseph, Z., et al., Computational discovery of gene modules and regulatory networks. Nat Biotechnol, 2003. 21(11): p. 1337−42.
10. Jansen, R., et al., A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science, 2003. 302(5644): p. 449−53.
11. Rhodes, D.R., et al., Probabilistic model of the human protein-protein interaction network. Nat Biotechnol, 2005. 23(8): p. 951−9.
12. Ekins, S., et al., Pathway mapping tools for analysis of high content data. Methods Mol Biol, 2007. 356: p. 319−50.
13. Nikolsky, Y., et al., A novel methodfor generation of signature networks as biomarkers from complex high throughput data. Toxicol Lett, 2005. 158(1): p. 209.
14. Nikolsky, Y., T. Nikolskaya, and A. Bugrim, Biological networks and analysis of experimental data in drug discovery. Drug Discov Today, 2005. 10(9): p. 653 662.
15. Dezso, Z., et al., A comprehensive functional analysis of tissue specificity of human gene expression. BMC Biol, 2008. 6: p. 49.
16. Nikolsky, Y. and J. Bryant, Protein networks and pathway analysis. Preface. Methods Mol Biol, 2009. 563: p. v-vii.
17. Osborne, C.K., et al., Estrogen receptor: current understanding of its activation and modulation. Clin Cancer Res, 2001. 7(12 Suppl): p. 4338s-4342sdiscussion 441 ls-4412s.
18. Greenman, C., et al., Patterns of somatic mutation in human cancer genomes. Nature, 2007. 446(7132): p. 153−8.
19. Scully, R. and A. Xie, BRCA1 and BRCA2 in breast cancer predisposition and recombination control. J Mammary Gland Biol Neoplasia, 2004. 9(3): p. 237−46.
20. Harris, C.C., p53 tumor suppressor gene: fi-om the basic research laboratory to the clinic—an abridged historical perspective. Carcinogenesis, 1996. 17(6): p. 1187−98.
21. Generali, D., et al., EGFR mutations in exons 18−21 in sporadic breast cancer. Ann Oncol, 2007. 18(1): p. 203−5.
22. Sato, M., et al., Multiple oncogenic changes (K-RAS (V12), p53 knockdown, mutant EGFRs, pi 6 bypass, telomerase) are not sufficient to confer a full malignantphenotype on human bronchial epithelial cells. Cancer Res, 2006. 66(4): p. 2116−28.
23. Markowitz, S., et al., Inactivation of the type II TGF-beta receptor in coloncancer cells with microsatellite instability. Science, 1995. 268(5215): p. 1336−8.136.
24. Rak, J., et al., Oncogenes as inducers of tumor angiogenesis. Cancer Metastasis Rev, 1995. 14(4): p. 263−77.
25. Zuo, L., et al., Germline mutations in thepl6INK4a binding domain of CDK4 in familial melanoma. Nat Genet, 1996. 12(1): p. 97−9.
26. Cantley, L.C. and B.G. Neel, New insights into tumor suppression: PTEN suppresses tumor formation by restraining the phosphoinositide 3-kinase/AKT pathway. Proc Natl Acad Sci USA, 1999. 96(8): p. 4240−5.
27. Sjoblom, T., et al., The consensus coding sequences of human breast and colorectal cancers. Science, 2006. 314(5797): p. 268−74.
28. Onay, V.U., et al., SNP-SNP interactions in breast cancer susceptibility. BMC Cancer, 2006. 6: p. 114.
29. Cox, A., et al., A common coding variant in CASP8 is associated with breast cancer risk. Nat Genet, 2007. 39(3): p. 352−8.
30. Easton, D.F., et al., Genome-wide association study identifies novel breast cancer susceptibility loci. Nature, 2007. 447(7148): p. 1087−93.
31. Hunter, D.J., et al., A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet, 2007. 39(7): p. 870−4.
32. Stacey, S.N., et al., Common variants on chromosomes 2q35 and 16ql2 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet, 2007. 39(7): p. 865−9.
33. Yao, J., et al., Combined cDNA array comparative genomic hybridization and serial analysis of gene expression analysis of breast tumor progression. Cancer Res, 2006. 66(8): p. 4065−78.
34. Shipitsin, M., et al., Molecular definition of breast tumor heterogeneity. Cancer Cell, 2007. 11(3): p. 259−73.
35. Gunnarsson, C., et al., Amplification of HSD17B1 and ERBB2 in primary breast cancer. Oncogene, 2003. 22(1): p. 34−40.
36. Kallioniemi, O.P., et al., ERBB2 amplification in breast cancer analyzed by fluorescence in situ hybridization. Proc Natl Acad Sci USA, 1992. 89(12): p. 5321−5.
37. Haverty, P.M., et al., High-resolution genomic and expression analyses of copy number alterations in breast tumors. Genes Chromosomes Cancer, 2008. 47(6): p. 530−42.
38. Bowles, E., et al., Profiling genomic copy number changes in retinoblastoma beyond loss of RBI. Genes Chromosomes Cancer, 2007. 46(2): p. 118−29.
39. McDonald, S.L., et al., Genomic changes identified by comparative genomic hybridisation in docetaxel-resistant breast cancer cell lines. Eur J Cancer, 2005. 41(7): p. 1086−94.
40. Miyakis, S. and D.A. Spandidos, Allelic loss in breast cancer. Cancer Detect Prev, 2002. 26(6): p. 426−34.
41. Sherr, C.J., Principles of tumor suppression. Cell, 2004. 116(2): p. 235−46.
42. Bird, A.P., CpG-rich islands and the function ofDNA methylation. Nature, 1986. 321(6067): p. 209−13.
43. Fuks, F., et al., DNA methyltransferase Dnmtl associates with histone deacetylase activity. Nat Genet, 2000. 24(1): p. 88−91.
44. Luther, T., et al., Identification of a novel urokinase receptor splice variant and its prognostic relevance in breast cancer. Thromb Haemost, 2003. 89(4): p. 70 517.
45. Skotheim, R.I. and M. Nees, Alternative splicing in cancer: noise, functional, or systematic? Int J Biochem Cell Biol, 2007. 39(7−8): p. 1432−49.
46. Holmila, R., et al., Splice mutations in the p53 gene: case report and review of the literature. HumMutat, 2003. 21(1): p. 101−2.
47. Popov, V.M., et al., The functional significance of nuclear receptor acetylation. Steroids, 2007. 72(2): p. 221−30.
48. Wu, F. and Y.Y. Mo, Ubiquitin-like protein modifications in prostate and breast cancer. Front Biosci, 2007. 12: p. 700−11.
49. IHGSC, Finishing the euchromatic sequence of the human genome. Nature, 2004. 431(7011): p. 931−45.
50. Galperin, M.Y., The Molecular Biology Database Collection: 2007 update. Nucleic Acids Res, 2007. 35(Database issue).
51. Bentley, D.R., Whole-genome re-sequencing. Curr Opin Genet Dev, 2006.16(6): p. 545−52.
52. Fedurco, M., et al., BT A, a novel reagent for DNA attachment on glass and efficient generation of solid-phase amplified DNA colonies. Nucleic Acids Res, 2006. 34(3): p. e22.
53. Shendure, J., et al., Accurate multiplex polony sequencing of an evolved bacterial genome. Science, 2005. 309(5741): p. 1728−32.
54. Pertea, M. and S.L. Salzberg, Between a chicken and a grape: estimating the number of human genes. Genome Biol, 2010. 11(5): p. 206.
55. Pruitt, K.D., et al., The consensus coding sequence (CCDS) project: Identifying a common protein-coding gene set for the human and mouse genomes. Genome Res, 2009. 19(7): p. 1316−23.
56. Pruitt, K.D., et al., NCBI Reference Sequences: current status, policy and new initiatives. Nucleic Acids Res, 2009. 37(Database issue): p. D32−6.
57. Hart, T., A. Ramani, and E. Marcotte, How complete are current yeast and human protein-interaction networks? Genome Biology, 2006. 7: p. 120.
58. Kemmeren, P., et al., Protein interaction verification andfunctional annotation by integrated analysis of genome-scale data. Mol Cell, 2002. 9(5): p. 1133−43.
59. Blaschke, C., et al., Automatic extraction of biological information from scientific text: protein-protein interactions. Proc Int Conf Intell Syst Mol Biol, 1999: p. 607.
60. 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.
61. Weigelt, B., F.L. Baehner, and J.S. Reis-Filho, The contribution of gene expression profiling to breast cancer classification, prognostication and prediction: a retrospective of the last decade. J Pathol, 2010. 220(2): p. 263−80.
62. Korkola, J.E., et al., Differentiation of lobular versus ductal breast carcinomas by expression microarray analysis. Cancer Res, 2003. 63(21): p. 7167−75.
63. Hedenfalk, I.A., et al., Gene expression in inherited breast cancer. Adv Cancer Res, 2002. 84: p. 1−34.
64. Zhao, H., et al., Different gene expression patterns in invasive lobular and ductal carcinomas of the breast. Mol Biol Cell, 2004. 15(6): p. 2523−36.
65. Michiels, S., S. Koscielny, and C. Hill, Interpretation of microarray data in cancer. Br J Cancer, 2007. 96(8): p. 1155−8.
66. Simon, R., et al., Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification. J Natl Cancer Inst, 2003. 95(1): p. 14−8.
67. Frantz, S., An array of problems. Nat Rev Drug Discov, 2005. 4(5): p. 362−3.
68. Ioannidis, J.P., Microarrays and molecular research: noise discovery? Lancet, 2005. 365(9458): p. 454−5.
69. Marshall, E., Getting the noise out of gene arrays. Science, 2004. 306(5696): p. 630−1.
70. Michiels, S., S. Koscielny, and C. Hill, Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet, 2005. 365(9458): p. 488−92.
71. Shi, L., et al., Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential. BMC Bioinformatics, 2005. 6 Suppl 2: p. S12.
72. Irizarry, R.A., et al., Mu Itiple-laboratory comparison of microarray platforms. Nat Methods, 2005. 2(5): p. 345−50.74.