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Items: 1 to 20 of 103

1.

Modified logistic regression models using gene coexpression and clinical features to predict prostate cancer progression.

Zhao H, Logothetis CJ, Gorlov IP, Zeng J, Dai J.

Comput Math Methods Med. 2013;2013:917502. doi: 10.1155/2013/917502. Epub 2013 Dec 4.

2.

Usefulness of the top-scoring pairs of genes for prediction of prostate cancer progression.

Zhao H, Logothetis CJ, Gorlov IP.

Prostate Cancer Prostatic Dis. 2010 Sep;13(3):252-9. doi: 10.1038/pcan.2010.9. Epub 2010 Apr 13.

3.

TSG: a new algorithm for binary and multi-class cancer classification and informative genes selection.

Wang H, Zhang H, Dai Z, Chen MS, Yuan Z.

BMC Med Genomics. 2013;6 Suppl 1:S3. doi: 10.1186/1755-8794-6-S1-S3. Epub 2013 Jan 23.

4.

Integration of gene expression profiling and clinical variables to predict prostate carcinoma recurrence after radical prostatectomy.

Stephenson AJ, Smith A, Kattan MW, Satagopan J, Reuter VE, Scardino PT, Gerald WL.

Cancer. 2005 Jul 15;104(2):290-8.

5.

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.

6.

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.

7.

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma.

Mount DW, Putnam CW, Centouri SM, Manziello AM, Pandey R, Garland LL, Martinez JD.

BMC Med Genomics. 2014 Jun 10;7:33. doi: 10.1186/1755-8794-7-33.

8.

Characterization of 1577 primary prostate cancers reveals novel biological and clinicopathologic insights into molecular subtypes.

Tomlins SA, Alshalalfa M, Davicioni E, Erho N, Yousefi K, Zhao S, Haddad Z, Den RB, Dicker AP, Trock BJ, DeMarzo AM, Ross AE, Schaeffer EM, Klein EA, Magi-Galluzzi C, Karnes RJ, Jenkins RB, Feng FY.

Eur Urol. 2015 Oct;68(4):555-67. doi: 10.1016/j.eururo.2015.04.033. Epub 2015 May 8.

9.

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
10.

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.

11.

MetaKTSP: a meta-analytic top scoring pair method for robust cross-study validation of omics prediction analysis.

Kim S, Lin CW, Tseng GC.

Bioinformatics. 2016 Jul 1;32(13):1966-73. doi: 10.1093/bioinformatics/btw115. Epub 2016 Mar 2.

PMID:
27153719
12.

Factor interaction analysis for chromosome 8 and DNA methylation alterations highlights innate immune response suppression and cytoskeletal changes in prostate cancer.

Schulz WA, Alexa A, Jung V, Hader C, Hoffmann MJ, Yamanaka M, Fritzsche S, Wlazlinski A, Müller M, Lengauer T, Engers R, Florl AR, Wullich B, Rahnenführer J.

Mol Cancer. 2007 Feb 5;6:14.

13.

ICP: A novel approach to predict prognosis of prostate cancer with inner-class clustering of gene expression data.

Kim H, Ahn J, Park C, Yoon Y, Park S.

Comput Biol Med. 2013 Oct;43(10):1363-73. doi: 10.1016/j.compbiomed.2013.06.014. Epub 2013 Jul 4.

PMID:
24034727
14.

Molecular sampling of prostate cancer: a dilemma for predicting disease progression.

Sboner A, Demichelis F, Calza S, Pawitan Y, Setlur SR, Hoshida Y, Perner S, Adami HO, Fall K, Mucci LA, Kantoff PW, Stampfer M, Andersson SO, Varenhorst E, Johansson JE, Gerstein MB, Golub TR, Rubin MA, Andrén O.

BMC Med Genomics. 2010 Mar 16;3:8. doi: 10.1186/1755-8794-3-8.

15.

Application of Affymetrix array and Massively Parallel Signature Sequencing for identification of genes involved in prostate cancer progression.

Oudes AJ, Roach JC, Walashek LS, Eichner LJ, True LD, Vessella RL, Liu AY.

BMC Cancer. 2005 Jul 22;5:86.

16.

Prioritizing candidate disease miRNAs by topological features in the miRNA target-dysregulated network: case study of prostate cancer.

Xu J, Li CX, Lv JY, Li YS, Xiao Y, Shao TT, Huo X, Li X, Zou Y, Han QL, Li X, Wang LH, Ren H.

Mol Cancer Ther. 2011 Oct;10(10):1857-66. doi: 10.1158/1535-7163.MCT-11-0055. Epub 2011 Jul 18.

17.

Genome-wide expression profiling reveals transcriptomic variation and perturbed gene networks in androgen-dependent and androgen-independent prostate cancer cells.

Singh AP, Bafna S, Chaudhary K, Venkatraman G, Smith L, Eudy JD, Johansson SL, Lin MF, Batra SK.

Cancer Lett. 2008 Jan 18;259(1):28-38. Epub 2007 Oct 30.

18.

Robust gene network analysis reveals alteration of the STAT5a network as a hallmark of prostate cancer.

Reddy A, Huang CC, Liu H, Delisi C, Nevalainen MT, Szalma S, Bhanot G.

Genome Inform. 2010;24:139-53.

PMID:
22081596
19.

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees.

Chou HL, Yao CT, Su SL, Lee CY, Hu KY, Terng HJ, Shih YW, Chang YT, Lu YF, Chang CW, Wahlqvist ML, Wetter T, Chu CM.

BMC Bioinformatics. 2013 Mar 19;14:100. doi: 10.1186/1471-2105-14-100.

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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