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Items: 1 to 20 of 92

1.

Coherent functional modules improve transcription factor target identification, cooperativity prediction, and disease association.

Karczewski KJ, Snyder M, Altman RB, Tatonetti NP.

PLoS Genet. 2014 Feb 6;10(2):e1004122. doi: 10.1371/journal.pgen.1004122.

3.

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.

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.

5.

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.

8.

Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data.

Li H, Zhan M.

Bioinformatics. 2008 Sep 1;24(17):1874-80. doi: 10.1093/bioinformatics/btn332.

9.

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.

10.

Integrating genomic data to predict transcription factor binding.

Holloway DT, Kon M, DeLisi C.

Genome Inform. 2005;16(1):83-94.

PMID:
16362910
11.

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.

12.

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.

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15.

A Computational Method for Identifying Yeast Cell Cycle Transcription Factors.

Wu WS.

Methods Mol Biol. 2016;1342:209-19. doi: 10.1007/978-1-4939-2957-3_12.

PMID:
26254926
16.

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.

PMID:
21703335
17.

Identifying cooperative transcription factors in yeast using multiple data sources.

Lai FJ, Jhu MH, Chiu CC, Huang YM, Wu WS.

BMC Syst Biol. 2014;8 Suppl 5:S2. doi: 10.1186/1752-0509-8-S5-S2.

18.

Regulatory network structure as a dominant determinant of transcription factor evolutionary rate.

Coulombe-Huntington J, Xia Y.

PLoS Comput Biol. 2012;8(10):e1002734. doi: 10.1371/journal.pcbi.1002734.

19.

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.

20.

A systems biology approach to the global analysis of transcription factors in colorectal cancer.

Pradhan MP, Prasad NK, Palakal MJ.

BMC Cancer. 2012 Aug 1;12:331. doi: 10.1186/1471-2407-12-331.

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