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Items: 1 to 20 of 59

1.

Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile.

Zhao C, Shi L, Tong W, Shaughnessy JD Jr, Oberthuer A, Pusztai L, Deng Y, Symmans WF, Shi T.

BMC Genomics. 2011 Dec 23;12 Suppl 5:S3. doi: 10.1186/1471-2164-12-S5-S3. Epub 2011 Dec 23.

2.

Intratumor heterogeneity and precision of microarray-based predictors of breast cancer biology and clinical outcome.

Barry WT, Kernagis DN, Dressman HK, Griffis RJ, Hunter JD, Olson JA, Marks JR, Ginsburg GS, Marcom PK, Nevins JR, Geradts J, Datto MB.

J Clin Oncol. 2010 May 1;28(13):2198-206. doi: 10.1200/JCO.2009.26.7245. Epub 2010 Apr 5.

3.

Outcome prediction based on microarray analysis: a critical perspective on methods.

Zervakis M, Blazadonakis ME, Tsiliki G, Danilatou V, Tsiknakis M, Kafetzopoulos D.

BMC Bioinformatics. 2009 Feb 7;10:53. doi: 10.1186/1471-2105-10-53.

4.

Gene expression profiles in esophageal adenocarcinoma predict survival after resection.

Pennathur A, Xi L, Litle VR, Gooding WE, Krasinskas A, Landreneau RJ, Godfrey TE, Luketich JD.

J Thorac Cardiovasc Surg. 2013 Feb;145(2):505-12; discussion 512-3. doi: 10.1016/j.jtcvs.2012.10.031.

5.

Gene expression profiling in multiple myeloma--reporting of entities, risk, and targets in clinical routine.

Meissner T, Seckinger A, Rème T, Hielscher T, Möhler T, Neben K, Goldschmidt H, Klein B, Hose D.

Clin Cancer Res. 2011 Dec 1;17(23):7240-7. doi: 10.1158/1078-0432.CCR-11-1628. Epub 2011 Oct 10.

6.

Signature Evaluation Tool (SET): a Java-based tool to evaluate and visualize the sample discrimination abilities of gene expression signatures.

Jen CH, Yang TP, Tung CY, Su SH, Lin CH, Hsu MT, Wang HW.

BMC Bioinformatics. 2008 Jan 28;9:58. doi: 10.1186/1471-2105-9-58.

7.

Gene expression profiling and correlation with outcome in clinical trials of the proteasome inhibitor bortezomib.

Mulligan G, Mitsiades C, Bryant B, Zhan F, Chng WJ, Roels S, Koenig E, Fergus A, Huang Y, Richardson P, Trepicchio WL, Broyl A, Sonneveld P, Shaughnessy JD Jr, Bergsagel PL, Schenkein D, Esseltine DL, Boral A, Anderson KC.

Blood. 2007 Apr 15;109(8):3177-88. Epub 2006 Dec 21.

8.

Low-risk identification in multiple myeloma using a new 14-gene model.

Chen T, Berno T, Zangari M.

Eur J Haematol. 2012 Jul;89(1):28-36. doi: 10.1111/j.1600-0609.2012.01792.x.

PMID:
22620863
9.

Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer.

Karlsson E, Delle U, Danielsson A, Olsson B, Abel F, Karlsson P, Helou K.

BMC Cancer. 2008 Sep 8;8:254. doi: 10.1186/1471-2407-8-254.

10.

Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.

Paciorek CJ, Liu Y; HEI Health Review Committee.

Res Rep Health Eff Inst. 2012 May;(167):5-83; discussion 85-91.

PMID:
22838153
11.

Predicting the prognosis of hepatocellular carcinoma using gene expression.

Chang SH, Suh KS, Yi NJ, Lee KH, Kim BY, Jang JJ.

J Surg Res. 2011 Dec;171(2):524-31. doi: 10.1016/j.jss.2010.05.023. Epub 2010 Jun 9.

PMID:
20828739
12.

Challenges in projecting clustering results across gene expression-profiling datasets.

Lusa L, McShane LM, Reid JF, De Cecco L, Ambrogi F, Biganzoli E, Gariboldi M, Pierotti MA.

J Natl Cancer Inst. 2007 Nov 21;99(22):1715-23. Epub 2007 Nov 13.

PMID:
18000217
13.

Joint analysis of two microarray gene-expression data sets to select lung adenocarcinoma marker genes.

Jiang H, Deng Y, Chen HS, Tao L, Sha Q, Chen J, Tsai CJ, Zhang S.

BMC Bioinformatics. 2004 Jun 24;5:81.

14.

Robust prognostic value of a knowledge-based proliferation signature across large patient microarray studies spanning different cancer types.

Starmans MH, Krishnapuram B, Steck H, Horlings H, Nuyten DS, van de Vijver MJ, Seigneuric R, Buffa FM, Harris AL, Wouters BG, Lambin P.

Br J Cancer. 2008 Dec 2;99(11):1884-90. doi: 10.1038/sj.bjc.6604746. Epub 2008 Nov 4.

15.

Insights from the gene expression profiling of multiple myeloma.

Claudio JO, Masih-Khan E, Stewart AK.

Curr Hematol Rep. 2004 Jan;3(1):67-73. Review.

PMID:
14695854
16.

Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.

Seo J, Bakay M, Chen YW, Hilmer S, Shneiderman B, Hoffman EP.

Bioinformatics. 2004 Nov 1;20(16):2534-44. Epub 2004 Apr 29.

PMID:
15117752
17.

A validated gene expression model of high-risk multiple myeloma is defined by deregulated expression of genes mapping to chromosome 1.

Shaughnessy JD Jr, Zhan F, Burington BE, Huang Y, Colla S, Hanamura I, Stewart JP, Kordsmeier B, Randolph C, Williams DR, Xiao Y, Xu H, Epstein J, Anaissie E, Krishna SG, Cottler-Fox M, Hollmig K, Mohiuddin A, Pineda-Roman M, Tricot G, van Rhee F, Sawyer J, Alsayed Y, Walker R, Zangari M, Crowley J, Barlogie B.

Blood. 2007 Mar 15;109(6):2276-84. Epub 2006 Nov 14.

18.

A prognostic gene expression profile that predicts circulating tumor cell presence in breast cancer patients.

Molloy TJ, Roepman P, Naume B, van't Veer LJ.

PLoS One. 2012;7(2):e32426. doi: 10.1371/journal.pone.0032426. Epub 2012 Feb 23.

19.

Converting a breast cancer microarray signature into a high-throughput diagnostic test.

Glas AM, Floore A, Delahaye LJ, Witteveen AT, Pover RC, Bakx N, Lahti-Domenici JS, Bruinsma TJ, Warmoes MO, Bernards R, Wessels LF, Van't Veer LJ.

BMC Genomics. 2006 Oct 30;7:278.

20.

Limits of predictive models using microarray data for breast cancer clinical treatment outcome.

Reid JF, Lusa L, De Cecco L, Coradini D, Veneroni S, Daidone MG, Gariboldi M, Pierotti MA.

J Natl Cancer Inst. 2005 Jun 15;97(12):927-30.

PMID:
15956654

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