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Items: 1 to 20 of 369

1.
2.

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.

3.

Systematic identification of yeast cell cycle transcription factors using multiple data sources.

Wu WS, Li WH.

BMC Bioinformatics. 2008 Dec 5;9:522. doi: 10.1186/1471-2105-9-522.

4.

Yeast cell cycle transcription factors identification by variable selection criteria.

Wang H, Wang YH, Wu WS.

Gene. 2011 Oct 10;485(2):172-6. doi: 10.1016/j.gene.2011.06.001. Epub 2011 Jun 16.

PMID:
21703335
5.
6.

Learning transcriptional networks from the integration of ChIP-chip and expression data in a non-parametric model.

Youn A, Reiss DJ, Stuetzle W.

Bioinformatics. 2010 Aug 1;26(15):1879-86. doi: 10.1093/bioinformatics/btq289. Epub 2010 Jun 4.

7.

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.

8.

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.

9.

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.

10.

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.

11.

Identifying cooperativity among transcription factors controlling the cell cycle in yeast.

Banerjee N, Zhang MQ.

Nucleic Acids Res. 2003 Dec 1;31(23):7024-31.

12.

Identifying combinatorial regulation of transcription factors and binding motifs.

Kato M, Hata N, Banerjee N, Futcher B, Zhang MQ.

Genome Biol. 2004;5(8):R56. Epub 2004 Jul 28.

13.

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.

14.

Quantitative characterization of the transcriptional regulatory network in the yeast cell cycle.

Chen HC, Lee HC, Lin TY, Li WH, Chen BS.

Bioinformatics. 2004 Aug 12;20(12):1914-27. Epub 2004 Mar 25.

15.
16.

Statistical methods for identifying yeast cell cycle transcription factors.

Tsai HK, Lu HH, Li WH.

Proc Natl Acad Sci U S A. 2005 Sep 20;102(38):13532-7. Epub 2005 Sep 12.

17.
18.

Identification of yeast transcriptional regulation networks using multivariate random forests.

Xiao Y, Segal MR.

PLoS Comput Biol. 2009 Jun;5(6):e1000414. doi: 10.1371/journal.pcbi.1000414. Epub 2009 Jun 19.

19.

An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data.

Ruan J, Deng Y, Perkins EJ, Zhang W.

BMC Genomics. 2009 Jul 7;10 Suppl 1:S8. doi: 10.1186/1471-2164-10-S1-S8.

20.

Integrating multiple types of data to predict novel cell cycle-related genes.

Wang L, Hou L, Qian M, Li F, Deng M.

BMC Syst Biol. 2011 Jun 20;5 Suppl 1:S9. doi: 10.1186/1752-0509-5-S1-S9.

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