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Items: 1 to 20 of 52

1.

Prediction of cardiac transcription networks based on molecular data and complex clinical phenotypes.

Toenjes M, Schueler M, Hammer S, Pape UJ, Fischer JJ, Berger F, Vingron M, Sperling S.

Mol Biosyst. 2008 Jun;4(6):589-98. doi: 10.1039/b800207j.

PMID:
18493657
2.

Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles.

Prieto C, Risueño A, Fontanillo C, De las Rivas J.

PLoS One. 2008;3(12):e3911. doi: 10.1371/journal.pone.0003911.

3.

Statistical identification of gene association by CID in application of constructing ER regulatory network.

Liu LY, Chen CY, Chen MJ, Tsai MS, Lee CH, Phang TL, Chang LY, Kuo WH, Hwa HL, Lien HC, Jung SM, Lin YS, Chang KJ, Hsieh FJ.

BMC Bioinformatics. 2009 Mar 17;10:85. doi: 10.1186/1471-2105-10-85.

4.

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.

PMID:
18976224
5.

Identifying promoter features of co-regulated genes with similar network motifs.

Harari O, del Val C, Romero-Zaliz R, Shin D, Huang H, Groisman EA, Zwir I.

BMC Bioinformatics. 2009 Apr 29;10 Suppl 4:S1. doi: 10.1186/1471-2105-10-S4-S1.

6.

Broad profiling of DNA-binding transcription factor activities improves regulatory network construction in adult mouse tissues.

Sun YM, Zhang Y, Zeng LQ, Wu JP, Wei L, Ren AH, Shao W, Qiao JY, Zhao YC, Zhang L, Mitchelson KR, Cheng J.

J Proteome Res. 2008 Oct;7(10):4455-64. doi: 10.1021/pr800417e.

PMID:
18759473
7.

Inferring direct regulatory targets of a transcription factor in the DREAM2 challenge.

Vega VB, Woo XY, Hamidi H, Yeo HC, Yeo ZX, Bourque G, Clarke ND.

Ann N Y Acad Sci. 2009 Mar;1158:215-23. doi: 10.1111/j.1749-6632.2008.03759.x.

PMID:
19348643
8.

Statistical analysis of dynamic transcriptional regulatory network structure.

Smith JJ, Saleem RA, Aitchison JD.

Methods Mol Biol. 2011;781:337-52. doi: 10.1007/978-1-61779-276-2_16.

PMID:
21877289
9.

Reconstruction of Escherichia coli transcriptional regulatory networks via regulon-based associations.

Zare H, Sangurdekar D, Srivastava P, Kaveh M, Khodursky A.

BMC Syst Biol. 2009 Apr 14;3:39. doi: 10.1186/1752-0509-3-39.

10.

A predictive model of the oxygen and heme regulatory network in yeast.

Kundaje A, Xin X, Lan C, Lianoglou S, Zhou M, Zhang L, Leslie C.

PLoS Comput Biol. 2008 Nov;4(11):e1000224. doi: 10.1371/journal.pcbi.1000224.

11.

A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas.

Mani KM, Lefebvre C, Wang K, Lim WK, Basso K, Dalla-Favera R, Califano A.

Mol Syst Biol. 2008;4:169. doi: 10.1038/msb.2008.2.

12.

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.

13.

Tissue-specific regulatory network extractor (TS-REX): a database and software resource for the tissue and cell type-specific investigation of transcription factor-gene networks.

Colecchia F, Kottwitz D, Wagner M, Pfenninger CV, Thiel G, Tamm I, Peterson C, Nuber UA.

Nucleic Acids Res. 2009 Jun;37(11):e82. doi: 10.1093/nar/gkp311.

14.

Combining experts in order to identify binding sites in yeast and mouse genomic data.

Robinson M, González Castellano C, Rezwan F, Adams R, Davey N, Rust A, Sun Y.

Neural Netw. 2008 Aug;21(6):856-61. doi: 10.1016/j.neunet.2008.07.004.

PMID:
18710795
15.

Prediction of pairwise gene interaction using threshold logic.

Gowda T, Vrudhula S, Kim S.

Ann N Y Acad Sci. 2009 Mar;1158:276-86. doi: 10.1111/j.1749-6632.2008.03763.x.

PMID:
19348649
16.

Genome-wide array analysis of normal and malformed human hearts.

Kaynak B, von Heydebreck A, Mebus S, Seelow D, Hennig S, Vogel J, Sperling HP, Pregla R, Alexi-Meskishvili V, Hetzer R, Lange PE, Vingron M, Lehrach H, Sperling S.

Circulation. 2003 May 20;107(19):2467-74.

17.

Cardiac function-related gene expression profiles in human atrial myocytes.

Ohki-Kaneda R, Ohashi J, Yamamoto K, Ueno S, Ota J, Choi YL, Koinuma K, Yamashita Y, Misawa Y, Fuse K, Ikeda U, Shimada K, Mano H.

Biochem Biophys Res Commun. 2004 Aug 6;320(4):1328-36.

PMID:
15303278
18.

Prediction of regulatory elements.

Sandelin A.

Methods Mol Biol. 2008;453:233-44. doi: 10.1007/978-1-60327-429-6_11.

PMID:
18712306
20.

Inference of active transcriptional networks by integration of gene expression kinetics modeling and multisource data.

Vu TT, Vohradsky J.

Genomics. 2009 May;93(5):426-33. doi: 10.1016/j.ygeno.2009.01.006.

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