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Items: 1 to 20 of 115

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.

Optimizing molecular signatures for predicting prostate cancer recurrence.

Sun Y, Goodison S.

Prostate. 2009 Jul 1;69(10):1119-27. doi: 10.1002/pros.20961.

5.

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.

6.
7.

Housekeeping genes in cancer: normalization of array data.

Khimani AH, Mhashilkar AM, Mikulskis A, O'Malley M, Liao J, Golenko EE, Mayer P, Chada S, Killian JB, Lott ST.

Biotechniques. 2005 May;38(5):739-45.

8.

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.

9.

Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer.

Ghosh D, Barette TR, Rhodes D, Chinnaiyan AM.

Funct Integr Genomics. 2003 Dec;3(4):180-8. Epub 2003 Jul 22.

PMID:
12884057
10.

An entropy-based gene selection method for cancer classification using microarray data.

Liu X, Krishnan A, Mondry A.

BMC Bioinformatics. 2005 Mar 24;6:76.

11.

Mixture modelling of gene expression data from microarray experiments.

Ghosh D, Chinnaiyan AM.

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

12.

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
13.

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.

14.

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.

15.

Combining Affymetrix microarray results.

Stevens JR, Doerge RW.

BMC Bioinformatics. 2005 Mar 17;6:57.

16.

[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
17.

Towards precise classification of cancers based on robust gene functional expression profiles.

Guo Z, Zhang T, Li X, Wang Q, Xu J, Yu H, Zhu J, Wang H, Wang C, Topol EJ, Wang Q, Rao S.

BMC Bioinformatics. 2005 Mar 17;6:58.

18.

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.

19.

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
20.
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