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Items: 1 to 20 of 72

1.

Uncovering transcription factor modules using one- and three-dimensional analyses.

Lan X, Farnham PJ, Jin VX.

J Biol Chem. 2012 Sep 7;287(37):30914-21. doi: 10.1074/jbc.R111.309229. Epub 2012 Sep 5. Review.

2.

Inter- and intra-combinatorial regulation by transcription factors and microRNAs.

Zhou Y, Ferguson J, Chang JT, Kluger Y.

BMC Genomics. 2007 Oct 30;8:396.

3.

Global analysis of gene transcription regulation in prokaryotes.

Zhou D, Yang R.

Cell Mol Life Sci. 2006 Oct;63(19-20):2260-90. Review.

PMID:
16927028
4.

Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data.

Zhang Y, Xuan J, de los Reyes BG, Clarke R, Ressom HW.

BMC Bioinformatics. 2008 Apr 21;9:203. doi: 10.1186/1471-2105-9-203.

5.

TF-centered downstream gene set enrichment analysis: Inference of causal regulators by integrating TF-DNA interactions and protein post-translational modifications information.

Liu Q, Tan Y, Huang T, Ding G, Tu Z, Liu L, Li Y, Dai H, Xie L.

BMC Bioinformatics. 2010 Dec 14;11 Suppl 11:S5. doi: 10.1186/1471-2105-11-S11-S5.

6.

Inferring transcriptional interactions and regulator activities from experimental data.

Wang RS, Zhang XS, Chen L.

Mol Cells. 2007 Dec 31;24(3):307-15. Review.

7.

COPS: detecting co-occurrence and spatial arrangement of transcription factor binding motifs in genome-wide datasets.

Ha N, Polychronidou M, Lohmann I.

PLoS One. 2012;7(12):e52055. doi: 10.1371/journal.pone.0052055. Epub 2012 Dec 18.

8.

Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data.

Liu X, Jessen WJ, Sivaganesan S, Aronow BJ, Medvedovic M.

BMC Bioinformatics. 2007 Aug 3;8:283.

9.

Identifying transcription factor targets using enhanced Bayesian classifier.

He D, Zhou D, Zhou Y.

Comput Biol Chem. 2007 Oct;31(5-6):355-60. Epub 2007 Aug 19.

PMID:
17890157
10.

Large-scale learning of combinatorial transcriptional dynamics from gene expression.

Asif HM, Sanguinetti G.

Bioinformatics. 2011 May 1;27(9):1277-83. doi: 10.1093/bioinformatics/btr113. Epub 2011 Mar 2.

11.

Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison.

Titsias MK, Honkela A, Lawrence ND, Rattray M.

BMC Syst Biol. 2012 May 30;6:53. doi: 10.1186/1752-0509-6-53.

12.

Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees.

Chen X, Blanchette M.

BMC Bioinformatics. 2007;8 Suppl 10:S2. doi: 10.1186/1471-2105-8-S10-S2.

13.
14.

Computational discovery of miR-TF regulatory modules in human genome.

Tran DH, Satou K, Ho TB, Pham TH.

Bioinformation. 2010 Feb 28;4(8):371-7.

15.

RIP: the regulatory interaction predictor--a machine learning-based approach for predicting target genes of transcription factors.

Bauer T, Eils R, K├Ânig R.

Bioinformatics. 2011 Aug 15;27(16):2239-47. doi: 10.1093/bioinformatics/btr366. Epub 2011 Jun 20.

16.

TF-Cluster: a pipeline for identifying functionally coordinated transcription factors via network decomposition of the shared coexpression connectivity matrix (SCCM).

Nie J, Stewart R, Zhang H, Thomson JA, Ruan F, Cui X, Wei H.

BMC Syst Biol. 2011 Apr 15;5:53. doi: 10.1186/1752-0509-5-53.

17.

Computational and experimental approaches for modeling gene regulatory networks.

Goutsias J, Lee NH.

Curr Pharm Des. 2007;13(14):1415-36. Review.

PMID:
17504165
18.

Predicting transcription factor synergism.

Hannenhalli S, Levy S.

Nucleic Acids Res. 2002 Oct 1;30(19):4278-84.

19.
20.

Regulatory network structure as a dominant determinant of transcription factor evolutionary rate.

Coulombe-Huntington J, Xia Y.

PLoS Comput Biol. 2012;8(10):e1002734. doi: 10.1371/journal.pcbi.1002734. Epub 2012 Oct 18.

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