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Results: 1 to 20 of 73

Similar articles for PubMed (Select 19630541)

1.

A combined expression-interaction model for inferring the temporal activity of transcription factors.

Shi Y, Klutstein M, Simon I, Mitchell T, Bar-Joseph Z.

J Comput Biol. 2009 Aug;16(8):1035-49. doi: 10.1089/cmb.2009.0024.

2.

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.

3.
4.

Identifying gene regulatory modules of heat shock response in yeast.

Wu WS, Li WH.

BMC Genomics. 2008 Sep 23;9:439. doi: 10.1186/1471-2164-9-439.

5.

Inferring condition-specific modulation of transcription factor activity in yeast through regulon-based analysis of genomewide expression.

Boorsma A, Lu XJ, Zakrzewska A, Klis FM, Bussemaker HJ.

PLoS One. 2008 Sep 3;3(9):e3112. doi: 10.1371/journal.pone.0003112.

6.

Inferring the regulatory interaction models of transcription factors in transcriptional regulatory networks.

Awad S, Panchy N, Ng SK, Chen J.

J Bioinform Comput Biol. 2012 Oct;10(5):1250012. doi: 10.1142/S0219720012500126. Epub 2012 Jun 26.

PMID:
22849367
7.

Identification of context-specific gene regulatory networks with GEMULA--gene expression modeling using LAsso.

Geeven G, van Kesteren RE, Smit AB, de Gunst MC.

Bioinformatics. 2012 Jan 15;28(2):214-21. doi: 10.1093/bioinformatics/btr641. Epub 2011 Nov 21.

8.

Quantifying transcriptional regulatory networks by integrating sequence features and microarray data.

Liu H.

Bioprocess Biosyst Eng. 2010 May;33(4):495-505. doi: 10.1007/s00449-009-0358-1. Epub 2009 Aug 6.

PMID:
19657679
10.

Inferring coregulation of transcription factors and microRNAs in breast cancer.

Wu JH, Sun YJ, Hsieh PH, Shieh GS.

Gene. 2013 Apr 10;518(1):139-44. doi: 10.1016/j.gene.2012.11.056. Epub 2012 Dec 14.

PMID:
23246694
11.

Inference of sparse combinatorial-control networks from gene-expression data: a message passing approach.

Bailly-Bechet M, Braunstein A, Pagnani A, Weigt M, Zecchina R.

BMC Bioinformatics. 2010 Jun 29;11:355. doi: 10.1186/1471-2105-11-355.

12.

Uncovering transcriptional interactions via an adaptive fuzzy logic approach.

Chuang CL, Hung K, Chen CM, Shieh GS.

BMC Bioinformatics. 2009 Dec 6;10:400. doi: 10.1186/1471-2105-10-400.

13.
14.

De novo motif discovery facilitates identification of interactions between transcription factors in Saccharomyces cerevisiae.

Chen MJ, Chou LC, Hsieh TT, Lee DD, Liu KW, Yu CY, Oyang YJ, Tsai HK, Chen CY.

Bioinformatics. 2012 Mar 1;28(5):701-8. doi: 10.1093/bioinformatics/bts002. Epub 2012 Jan 11.

15.

Transcriptome network component analysis with limited microarray data.

Galbraith SJ, Tran LM, Liao JC.

Bioinformatics. 2006 Aug 1;22(15):1886-94. Epub 2006 Jun 9.

16.
17.

Systematic identification of cell cycle regulated transcription factors from microarray time series data.

Cheng C, Li LM.

BMC Genomics. 2008 Mar 3;9:116. doi: 10.1186/1471-2164-9-116.

18.

Exploiting combinatorial cultivation conditions to infer transcriptional regulation.

Knijnenburg TA, de Winde JH, Daran JM, Daran-Lapujade P, Pronk JT, Reinders MJ, Wessels LF.

BMC Genomics. 2007 Jan 22;8:25.

19.

Transcriptional regulatory networks via gene ontology and expression data.

Tuncay K, Ensman L, Sun J, Haidar AA, Stanley F, Trelinski M, Ortoleva P.

In Silico Biol. 2007;7(1):21-34.

PMID:
17688426
20.

Identification of interacting transcription factors regulating tissue gene expression in human.

Hu Z, Gallo SM.

BMC Genomics. 2010 Jan 19;11:49. doi: 10.1186/1471-2164-11-49.

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