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Items: 1 to 20 of 78


Age-adjusted nonparametric detection of differential DNA methylation with case-control designs.

Huang H, Chen Z, Huang X.

BMC Bioinformatics. 2013 Mar 6;14:86. doi: 10.1186/1471-2105-14-86.


A new statistical approach to detecting differentially methylated loci for case control Illumina array methylation data.

Chen Z, Liu Q, Nadarajah S.

Bioinformatics. 2012 Apr 15;28(8):1109-13. doi: 10.1093/bioinformatics/bts093. Epub 2012 Feb 24.


Method to detect differentially methylated loci with case-control designs using Illumina arrays.

Wang S.

Genet Epidemiol. 2011 Nov;35(7):686-94. doi: 10.1002/gepi.20619. Epub 2011 Aug 4.


Detecting differentially methylated loci for multiple treatments based on high-throughput methylation data.

Chen Z, Huang H, Liu Q.

BMC Bioinformatics. 2014 May 15;15:142. doi: 10.1186/1471-2105-15-142.


Detecting differentially methylated loci for Illumina Array methylation data based on human ovarian cancer data.

Chen Z, Huang H, Liu J, Tony Ng HK, Nadarajah S, Huang X, Deng Y.

BMC Med Genomics. 2013;6 Suppl 1:S9. doi: 10.1186/1755-8794-6-S1-S9. Epub 2013 Jan 23.


An evaluation of statistical methods for DNA methylation microarray data analysis.

Li D, Xie Z, Pape ML, Dye T.

BMC Bioinformatics. 2015 Jul 10;16:217. doi: 10.1186/s12859-015-0641-x.


Semiparametric tests for identifying differentially methylated loci with case-control designs using Illumina arrays.

Chen Y, Ning Y, Hong C, Wang S.

Genet Epidemiol. 2014 Jan;38(1):42-50. doi: 10.1002/gepi.21774. Epub 2013 Dec 3.


Quantitative identification of differentially methylated loci based on relative entropy for matched case-control data.

Zhang Y, Zhang J, Shang J.

Epigenomics. 2013 Dec;5(6):631-43. doi: 10.2217/epi.13.58.


Nonparametric Bayesian clustering to detect bipolar methylated genomic loci.

Wu X, Sun MA, Zhu H, Xie H.

BMC Bioinformatics. 2015 Jan 16;16:11. doi: 10.1186/s12859-014-0439-2.


Penalized logistic regression for high-dimensional DNA methylation data with case-control studies.

Sun H, Wang S.

Bioinformatics. 2012 May 15;28(10):1368-75. doi: 10.1093/bioinformatics/bts145. Epub 2012 Mar 30.


Peripheral blood immune cell methylation profiles are associated with nonhematopoietic cancers.

Koestler DC, Marsit CJ, Christensen BC, Accomando W, Langevin SM, Houseman EA, Nelson HH, Karagas MR, Wiencke JK, Kelsey KT.

Cancer Epidemiol Biomarkers Prev. 2012 Aug;21(8):1293-302. doi: 10.1158/1055-9965.EPI-12-0361. Epub 2012 Jun 19.


Methylation Linear Discriminant Analysis (MLDA) for identifying differentially methylated CpG islands.

Dai W, Teodoridis JM, Graham J, Zeller C, Huang TH, Yan P, Vass JK, Brown R, Paul J.

BMC Bioinformatics. 2008 Aug 8;9:337. doi: 10.1186/1471-2105-9-337.


Promoter targeted bisulfite sequencing reveals DNA methylation profiles associated with low sperm motility in asthenozoospermia.

Du Y, Li M, Chen J, Duan Y, Wang X, Qiu Y, Cai Z, Gui Y, Jiang H.

Hum Reprod. 2016 Jan;31(1):24-33. doi: 10.1093/humrep/dev283. Epub 2015 Nov 30.


Network-based regularization for matched case-control analysis of high-dimensional DNA methylation data.

Sun H, Wang S.

Stat Med. 2013 May 30;32(12):2127-39. doi: 10.1002/sim.5694. Epub 2012 Dec 5.


Quantitative detection of RASSF1A DNA promoter methylation in tumors and serum of patients with serous epithelial ovarian cancer.

Bondurant AE, Huang Z, Whitaker RS, Simel LR, Berchuck A, Murphy SK.

Gynecol Oncol. 2011 Dec;123(3):581-7. doi: 10.1016/j.ygyno.2011.08.029. Epub 2011 Sep 28.


A method to detect differentially methylated loci with next-generation sequencing.

Xu H, Podolsky RH, Ryu D, Wang X, Su S, Shi H, George V.

Genet Epidemiol. 2013 May;37(4):377-82. doi: 10.1002/gepi.21726. Epub 2013 Apr 1.


Genome-scale screen for DNA methylation-based detection markers for ovarian cancer.

Campan M, Moffitt M, Houshdaran S, Shen H, Widschwendter M, Daxenbichler G, Long T, Marth C, Laird-Offringa IA, Press MF, Dubeau L, Siegmund KD, Wu AH, Groshen S, Chandavarkar U, Roman LD, Berchuck A, Pearce CL, Laird PW.

PLoS One. 2011;6(12):e28141. doi: 10.1371/journal.pone.0028141. Epub 2011 Dec 7.


Differential variability improves the identification of cancer risk markers in DNA methylation studies profiling precursor cancer lesions.

Teschendorff AE, Widschwendter M.

Bioinformatics. 2012 Jun 1;28(11):1487-94. doi: 10.1093/bioinformatics/bts170. Epub 2012 Apr 6.


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