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Items: 1 to 20 of 431

1.

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.

2.

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.

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A classification-based framework for predicting and analyzing gene regulatory response.

Kundaje A, Middendorf M, Shah M, Wiggins CH, Freund Y, Leslie C.

BMC Bioinformatics. 2006 Mar 20;7 Suppl 1:S5.

5.

IRIS: a method for reverse engineering of regulatory relations in gene networks.

Morganella S, Zoppoli P, Ceccarelli M.

BMC Bioinformatics. 2009 Dec 23;10:444. doi: 10.1186/1471-2105-10-444.

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TimeDelay-ARACNE: Reverse engineering of gene networks from time-course data by an information theoretic approach.

Zoppoli P, Morganella S, Ceccarelli M.

BMC Bioinformatics. 2010 Mar 25;11:154. doi: 10.1186/1471-2105-11-154.

8.

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.

9.

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.

10.

Statistical methods in integrative analysis for gene regulatory modules.

Zeng L, Wu J, Xie J.

Stat Appl Genet Mol Biol. 2008;7(1):Article 28. doi: 10.2202/1544-6115.1369. Epub 2008 Oct 10.

PMID:
18976224
11.
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.

13.

Uncovering transcriptional interactions via an adaptive fuzzy logic approach.

Chuang CL, Hung K, Chen CM, Shieh GS.

BMC Bioinformatics. 2009 Dec 6;10:400. doi: 10.1186/1471-2105-10-400.

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16.

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.

17.

Inferring gene regulatory networks by integrating static and dynamic data.

Ferrazzi F, Magni P, Sacchi L, Nuzzo A, Petrovic U, Bellazzi R.

Int J Med Inform. 2007 Dec;76 Suppl 3:S462-75. Epub 2007 Sep 6.

PMID:
17825607
19.

Identification of temporal association rules from time-series microarray data sets.

Nam H, Lee K, Lee D.

BMC Bioinformatics. 2009 Mar 19;10 Suppl 3:S6. doi: 10.1186/1471-2105-10-S3-S6.

20.

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