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Items: 1 to 20 of 369


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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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