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Items: 1 to 20 of 207

1.

Motif-guided sparse decomposition of gene expression data for regulatory module identification.

Gong T, Xuan J, Chen L, Riggins RB, Li H, Hoffman EP, Clarke R, Wang Y.

BMC Bioinformatics. 2011 Mar 22;12:82. doi: 10.1186/1471-2105-12-82.

2.

mAPC-GibbsOS: an integrated approach for robust identification of gene regulatory networks.

Shi X, Gu J, Chen X, Shajahan A, Hilakivi-Clarke L, Clarke R, Xuan J.

BMC Syst Biol. 2013;7 Suppl 5:S4. doi: 10.1186/1752-0509-7-S5-S4.

3.

Multilevel support vector regression analysis to identify condition-specific regulatory networks.

Chen L, Xuan J, Riggins RB, Wang Y, Hoffman EP, Clarke R.

Bioinformatics. 2010 Jun 1;26(11):1416-22. doi: 10.1093/bioinformatics/btq144.

4.

Defining transcription modules using large-scale gene expression data.

Ihmels J, Bergmann S, Barkai N.

Bioinformatics. 2004 Sep 1;20(13):1993-2003.

PMID:
15044247
5.

Knowledge-guided multi-scale independent component analysis for biomarker identification.

Chen L, Xuan J, Wang C, Shih IeM, Wang Y, Zhang Z, Hoffman E, Clarke R.

BMC Bioinformatics. 2008 Oct 6;9:416. doi: 10.1186/1471-2105-9-416.

6.

Incorporating motif analysis into gene co-expression networks reveals novel modular expression pattern and new signaling pathways.

Ma S, Shah S, Bohnert HJ, Snyder M, Dinesh-Kumar SP.

PLoS Genet. 2013;9(10):e1003840. doi: 10.1371/journal.pgen.1003840.

7.

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.

8.

Identification of condition-specific regulatory modules through multi-level motif and mRNA expression analysis.

Chen L, Xuan J, Wang Y, Hoffman EP, Riggins RB, Clarke R.

Int J Comput Biol Drug Des. 2009;2(1):1-20.

9.

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification.

Zhang W, Edwards A, Fan W, Zhu D, Zhang K.

BMC Bioinformatics. 2010 Jun 22;11:338. doi: 10.1186/1471-2105-11-338.

10.

Prioritization of gene regulatory interactions from large-scale modules in yeast.

Lee HJ, Manke T, Bringas R, Vingron M.

BMC Bioinformatics. 2008 Jan 22;9:32. doi: 10.1186/1471-2105-9-32.

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Combining sequence and time series expression data to learn transcriptional modules.

Kundaje A, Middendorf M, Gao F, Wiggins C, Leslie C.

IEEE/ACM Trans Comput Biol Bioinform. 2005 Jul-Sep;2(3):194-202.

PMID:
17044183
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14.

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.

15.

A new clustering approach for learning transcriptional modules.

Archetti F, Giordani I, Mauri G, Messina E.

Int J Data Min Bioinform. 2012;6(3):304-23.

PMID:
23155764
16.

Regulatory Snapshots: integrative mining of regulatory modules from expression time series and regulatory networks.

Gonçalves JP, Aires RS, Francisco AP, Madeira SC.

PLoS One. 2012;7(5):e35977. doi: 10.1371/journal.pone.0035977.

17.

An integrative approach to infer regulation programs in a transcription regulatory module network.

Qi J, Michoel T, Butler G.

J Biomed Biotechnol. 2012;2012:245968. doi: 10.1155/2012/245968.

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