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Items: 1 to 20 of 111

1.

A Bayesian hidden Markov model for motif discovery through joint modeling of genomic sequence and ChIP-chip data.

Gelfond JA, Gupta M, Ibrahim JG.

Biometrics. 2009 Dec;65(4):1087-95. doi: 10.1111/j.1541-0420.2008.01180.x.

2.

A transdimensional Bayesian model for pattern recognition in DNA sequences.

Li SM, Wakefield J, Self S.

Biostatistics. 2008 Oct;9(4):668-85. doi: 10.1093/biostatistics/kxm058.

3.
4.

Distinguishing direct versus indirect transcription factor-DNA interactions.

Gordân R, Hartemink AJ, Bulyk ML.

Genome Res. 2009 Nov;19(11):2090-100. doi: 10.1101/gr.094144.109.

5.
6.

Bayesian modeling of ChIP-chip data through a high-order Ising model.

Mo Q, Liang F.

Biometrics. 2010 Dec;66(4):1284-94. doi: 10.1111/j.1541-0420.2009.01379.x.

PMID:
20128774
7.

A fully Bayesian hidden Ising model for ChIP-seq data analysis.

Mo Q.

Biostatistics. 2012 Jan;13(1):113-28. doi: 10.1093/biostatistics/kxr029.

8.

Transcription factor binding site identification in yeast: a comparison of high-density oligonucleotide and PCR-based microarray platforms.

Borneman AR, Zhang ZD, Rozowsky J, Seringhaus MR, Gerstein M, Snyder M.

Funct Integr Genomics. 2007 Oct;7(4):335-45.

PMID:
17638031
9.

Heterogeneity in DNA multiple alignments: modeling, inference, and applications in motif finding.

Chen G, Zhou Q.

Biometrics. 2010 Sep;66(3):694-704. doi: 10.1111/j.1541-0420.2009.01362.x.

PMID:
19995355
10.

Genome-wide protein-DNA binding dynamics suggest a molecular clutch for transcription factor function.

Lickwar CR, Mueller F, Hanlon SE, McNally JG, Lieb JD.

Nature. 2012 Apr 11;484(7393):251-5. doi: 10.1038/nature10985.

11.

A temporal hidden Markov regression model for the analysis of gene regulatory networks.

Gupta M, Qu P, Ibrahim JG.

Biostatistics. 2007 Oct;8(4):805-20.

12.

A Monte Carlo-based framework enhances the discovery and interpretation of regulatory sequence motifs.

Seitzer P, Wilbanks EG, Larsen DJ, Facciotti MT.

BMC Bioinformatics. 2012 Nov 27;13:317. doi: 10.1186/1471-2105-13-317.

13.

A Bayesian search for transcriptional motifs.

Miller AK, Print CG, Nielsen PM, Crampin EJ.

PLoS One. 2010 Nov 18;5(11):e13897. doi: 10.1371/journal.pone.0013897.

14.

On the detection and refinement of transcription factor binding sites using ChIP-Seq data.

Hu M, Yu J, Taylor JM, Chinnaiyan AM, Qin ZS.

Nucleic Acids Res. 2010 Apr;38(7):2154-67. doi: 10.1093/nar/gkp1180.

15.

Promoter region-based classification of genes.

Pavlidis P, Furey TS, Liberto M, Haussler D, Grundy WN.

Pac Symp Biocomput. 2001:151-63.

16.

Integrative analyses for omics data: a Bayesian mixture model to assess the concordance of ChIP-chip and ChIP-seq measurements.

Schäfer M, Lkhagvasuren O, Klein HU, Elling C, Wüstefeld T, Müller-Tidow C, Zender L, Koschmieder S, Dugas M, Ickstadt K.

J Toxicol Environ Health A. 2012;75(8-10):461-70. doi: 10.1080/15287394.2012.674914.

PMID:
22686305
17.

ChIP-chip to analyze the binding of replication proteins to chromatin using oligonucleotide DNA microarrays.

Viggiani CJ, Aparicio JG, Aparicio OM.

Methods Mol Biol. 2009;521:255-78. doi: 10.1007/978-1-60327-815-7_14.

PMID:
19563111
18.

Characterization of the yeast telomere nucleoprotein core: Rap1 binds independently to each recognition site.

Williams TL, Levy DL, Maki-Yonekura S, Yonekura K, Blackburn EH.

J Biol Chem. 2010 Nov 12;285(46):35814-24. doi: 10.1074/jbc.M110.170167.

19.

Extracting transcription factor binding sites from unaligned gene sequences with statistical models.

Lu CC, Yuan WH, Chen TM.

BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S7. doi: 10.1186/1471-2105-9-S12-S7.

20.

PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny.

Siddharthan R, Siggia ED, van Nimwegen E.

PLoS Comput Biol. 2005 Dec;1(7):e67.

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