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Items: 1 to 20 of 104

1.

Molecular signature of cancer at gene level or pathway level? Case studies of colorectal cancer and prostate cancer microarray data.

Chen J, Wang Y, Shen B, Zhang D.

Comput Math Methods Med. 2013;2013:909525. doi: 10.1155/2013/909525. Epub 2013 Jan 16.

2.

Identifying novel prostate cancer associated pathways based on integrative microarray data analysis.

Wang Y, Chen J, Li Q, Wang H, Liu G, Jing Q, Shen B.

Comput Biol Chem. 2011 Jun;35(3):151-8. doi: 10.1016/j.compbiolchem.2011.04.003. Epub 2011 Apr 27.

PMID:
21704261
3.

Microarrays--identifying molecular portraits for prostate tumors with different Gleason patterns.

Mendes A, Scott RJ, Moscato P.

Methods Mol Med. 2008;141:131-51. Review.

PMID:
18453088
4.

A stochastic model for identifying differential gene pair co-expression patterns in prostate cancer progression.

Mo WJ, Fu XP, Han XT, Yang GY, Zhang JG, Guo FH, Huang Y, Mao YM, Li Y, Xie Y.

BMC Genomics. 2009 Jul 29;10:340. doi: 10.1186/1471-2164-10-340.

5.
6.

In silico microdissection of microarray data from heterogeneous cell populations.

Lähdesmäki H, Shmulevich L, Dunmire V, Yli-Harja O, Zhang W.

BMC Bioinformatics. 2005 Mar 14;6:54.

7.

Mixture modelling of gene expression data from microarray experiments.

Ghosh D, Chinnaiyan AM.

Bioinformatics. 2002 Feb;18(2):275-86.

PMID:
11847075
8.

Microarray data analysis: from disarray to consolidation and consensus.

Allison DB, Cui X, Page GP, Sabripour M.

Nat Rev Genet. 2006 Jan;7(1):55-65. Review. Erratum in: Nat Rev Genet. 2006 May;7(5):406.

PMID:
16369572
9.

Identifying differential correlation in gene/pathway combinations.

Braun R, Cope L, Parmigiani G.

BMC Bioinformatics. 2008 Nov 18;9:488. doi: 10.1186/1471-2105-9-488.

10.

Comparison of seven methods for producing Affymetrix expression scores based on False Discovery Rates in disease profiling data.

Shedden K, Chen W, Kuick R, Ghosh D, Macdonald J, Cho KR, Giordano TJ, Gruber SB, Fearon ER, Taylor JM, Hanash S.

BMC Bioinformatics. 2005 Feb 10;6:26.

11.

Combining Affymetrix microarray results.

Stevens JR, Doerge RW.

BMC Bioinformatics. 2005 Mar 17;6:57.

12.

[Key genes in the pathogenesis of prostate cancer in Chinese men: a bioinformatic study].

Wang G, Yang K, Meng S, Xu Y, Yang ZH, Liu Y.

Zhonghua Nan Ke Xue. 2010 Apr;16(4):320-4. Chinese.

PMID:
20626159
13.

Empirical Bayes screening of many p-values with applications to microarray studies.

Datta S, Datta S.

Bioinformatics. 2005 May 1;21(9):1987-94. Epub 2005 Feb 2.

PMID:
15691856
14.

Discovery of prostate cancer biomarkers by microarray gene expression profiling.

Sørensen KD, Ørntoft TF.

Expert Rev Mol Diagn. 2010 Jan;10(1):49-64. doi: 10.1586/erm.09.74. Review.

PMID:
20014922
15.
17.

Gene expression profiles in prostate cancer: association with patient subgroups and tumour differentiation.

Halvorsen OJ, Oyan AM, Bø TH, Olsen S, Rostad K, Haukaas SA, Bakke AM, Marzolf B, Dimitrov K, Stordrange L, Lin B, Jonassen I, Hood L, Akslen LA, Kalland KH.

Int J Oncol. 2005 Feb;26(2):329-36.

PMID:
15645116
18.

Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer.

Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM.

Cancer Res. 2002 Aug 1;62(15):4427-33.

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20.

Integrating biomedical knowledge to model pathways of prostate cancer progression.

Morris DS, Tomlins SA, Rhodes DR, Mehra R, Shah RB, Chinnaiyan AM.

Cell Cycle. 2007 May 15;6(10):1177-87. Epub 2007 May 5. Review.

PMID:
17495538

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