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Items: 1 to 20 of 113

1.

In silico evaluation of predicted regulatory interactions in Arabidopsis thaliana.

Nero D, Katari MS, Kelfer J, Tranchina D, Coruzzi GM.

BMC Bioinformatics. 2009 Dec 21;10:435. doi: 10.1186/1471-2105-10-435.

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Prediction of regulatory interactions in Arabidopsis using gene-expression data and support vector machines.

Yu X, Liu T, Zheng X, Yang Z, Wang J.

Plant Physiol Biochem. 2011 Mar;49(3):280-3. doi: 10.1016/j.plaphy.2011.01.002.

PMID:
21282061
4.

Identification of transcription factor's targets using tissue-specific transcriptomic data in Arabidopsis thaliana.

Srivastava GP, Li P, Liu J, Xu D.

BMC Syst Biol. 2010 Sep 13;4 Suppl 2:S2. doi: 10.1186/1752-0509-4-S2-S2.

5.

Systematic identification of functional modules and cis-regulatory elements in Arabidopsis thaliana.

Ruan J, Perez J, Hernandez B, Lei C, Sunter G, Sponsel VM.

BMC Bioinformatics. 2011 Nov 24;12 Suppl 12:S2. doi: 10.1186/1471-2105-12-S12-S2.

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A biophysical model for analysis of transcription factor interaction and binding site arrangement from genome-wide binding data.

He X, Chen CC, Hong F, Fang F, Sinha S, Ng HH, Zhong S.

PLoS One. 2009 Dec 1;4(12):e8155. doi: 10.1371/journal.pone.0008155.

8.

Constructing gene regulatory networks for long term photosynthetic light acclimation in Arabidopsis thaliana.

Yao CW, Hsu BD, Chen BS.

BMC Bioinformatics. 2011 Aug 11;12:335. doi: 10.1186/1471-2105-12-335.

9.

Prediction of transcriptional regulatory elements for plant hormone responses based on microarray data.

Yamamoto YY, Yoshioka Y, Hyakumachi M, Maruyama K, Yamaguchi-Shinozaki K, Tokizawa M, Koyama H.

BMC Plant Biol. 2011 Feb 24;11:39. doi: 10.1186/1471-2229-11-39.

10.

Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis.

Ma C, Wang X.

Plant Physiol. 2012 Sep;160(1):192-203. doi: 10.1104/pp.112.201962.

11.

Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory.

Luo F, Yang Y, Zhong J, Gao H, Khan L, Thompson DK, Zhou J.

BMC Bioinformatics. 2007 Aug 14;8:299.

12.

Expression-based network biology identifies immune-related functional modules involved in plant defense.

Tully JP, Hill AE, Ahmed HM, Whitley R, Skjellum A, Mukhtar MS.

BMC Genomics. 2014 Jun 3;15:421. doi: 10.1186/1471-2164-15-421.

13.

Quantitative inference of dynamic regulatory pathways via microarray data.

Chang WC, Li CW, Chen BS.

BMC Bioinformatics. 2005 Mar 7;6:44.

14.

A brassinosteroid transcriptional network revealed by genome-wide identification of BESI target genes in Arabidopsis thaliana.

Yu X, Li L, Zola J, Aluru M, Ye H, Foudree A, Guo H, Anderson S, Aluru S, Liu P, Rodermel S, Yin Y.

Plant J. 2011 Feb;65(4):634-46. doi: 10.1111/j.1365-313X.2010.04449.x.

15.

Quantitative RNA expression analysis with Affymetrix Tiling 1.0R arrays identifies new E2F target genes.

Naouar N, Vandepoele K, Lammens T, Casneuf T, Zeller G, van Hummelen P, Weigel D, Rätsch G, Inzé D, Kuiper M, De Veylder L, Vuylsteke M.

Plant J. 2009 Jan;57(1):184-94. doi: 10.1111/j.1365-313X.2008.03662.x.

17.

A transcriptional dynamic network during Arabidopsis thaliana pollen development.

Wang J, Qiu X, Li Y, Deng Y, Shi T.

BMC Syst Biol. 2011;5 Suppl 3:S8. doi: 10.1186/1752-0509-5-S3-S8.

18.

Genome-wide identification of osmotic stress response gene in Arabidopsis thaliana.

Li Y, Zhu Y, Liu Y, Shu Y, Meng F, Lu Y, Bai X, Liu B, Guo D.

Genomics. 2008 Dec;92(6):488-93. doi: 10.1016/j.ygeno.2008.08.011.

19.

CORNET 2.0: integrating plant coexpression, protein-protein interactions, regulatory interactions, gene associations and functional annotations.

De Bodt S, Hollunder J, Nelissen H, Meulemeester N, Inzé D.

New Phytol. 2012 Aug;195(3):707-20. doi: 10.1111/j.1469-8137.2012.04184.x.

20.

Combining microarray and genomic data to predict DNA binding motifs.

Mao L, Mackenzie C, Roh JH, Eraso JM, Kaplan S, Resat H.

Microbiology. 2005 Oct;151(Pt 10):3197-213.

PMID:
16207904
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