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Items: 1 to 20 of 381

1.

Learning gene networks under SNP perturbations using eQTL datasets.

Zhang L, Kim S.

PLoS Comput Biol. 2014 Feb 27;10(2):e1003420. doi: 10.1371/journal.pcbi.1003420. eCollection 2014 Feb. Erratum in: PLoS Comput Biol. 2014 Apr;10(4):e1003608.

2.

Gene expression network reconstruction by convex feature selection when incorporating genetic perturbations.

Logsdon BA, Mezey J.

PLoS Comput Biol. 2010 Dec 2;6(12):e1001014. doi: 10.1371/journal.pcbi.1001014.

3.

Inference of SNP-gene regulatory networks by integrating gene expressions and genetic perturbations.

Kim DC, Wang J, Liu C, Gao J.

Biomed Res Int. 2014;2014:629697. doi: 10.1155/2014/629697. Epub 2014 Jun 9.

4.

Inference of gene regulatory networks with sparse structural equation models exploiting genetic perturbations.

Cai X, Bazerque JA, Giannakis GB.

PLoS Comput Biol. 2013;9(5):e1003068. doi: 10.1371/journal.pcbi.1003068. Epub 2013 May 23.

5.

Fast and robust group-wise eQTL mapping using sparse graphical models.

Cheng W, Shi Y, Zhang X, Wang W.

BMC Bioinformatics. 2015 Jan 16;16:2. doi: 10.1186/s12859-014-0421-z.

6.

Gene network inference via structural equation modeling in genetical genomics experiments.

Liu B, de la Fuente A, Hoeschele I.

Genetics. 2008 Mar;178(3):1763-76. doi: 10.1534/genetics.107.080069. Epub 2008 Feb 3.

7.

Mapping eQTL networks with mixed graphical Markov models.

Tur I, Roverato A, Castelo R.

Genetics. 2014 Dec;198(4):1377-93. doi: 10.1534/genetics.114.169573. Epub 2014 Sep 29.

8.

High-confidence discovery of genetic network regulators in expression quantitative trait loci data.

Duarte CW, Zeng ZB.

Genetics. 2011 Mar;187(3):955-64. doi: 10.1534/genetics.110.124685. Epub 2011 Jan 6.

9.

Genetical genomics analysis of a yeast segregant population for transcription network inference.

Bing N, Hoeschele I.

Genetics. 2005 Jun;170(2):533-42. Epub 2005 Mar 21.

10.
11.

An effective framework for reconstructing gene regulatory networks from genetical genomics data.

Flassig RJ, Heise S, Sundmacher K, Klamt S.

Bioinformatics. 2013 Jan 15;29(2):246-54. doi: 10.1093/bioinformatics/bts679. Epub 2012 Nov 21.

PMID:
23175757
12.

Leveraging input and output structures for joint mapping of epistatic and marginal eQTLs.

Lee S, Xing EP.

Bioinformatics. 2012 Jun 15;28(12):i137-46. doi: 10.1093/bioinformatics/bts227.

13.

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. Epub 2008 Nov 14.

14.

Learning a prior on regulatory potential from eQTL data.

Lee SI, Dudley AM, Drubin D, Silver PA, Krogan NJ, Pe'er D, Koller D.

PLoS Genet. 2009 Jan;5(1):e1000358. doi: 10.1371/journal.pgen.1000358. Epub 2009 Jan 30.

15.

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.

Ju JH, Shenoy SA, Crystal RG, Mezey JG.

PLoS Comput Biol. 2017 May 15;13(5):e1005537. doi: 10.1371/journal.pcbi.1005537. eCollection 2017 May.

16.

A copula method for modeling directional dependence of genes.

Kim JM, Jung YS, Sungur EA, Han KH, Park C, Sohn I.

BMC Bioinformatics. 2008 May 1;9:225. doi: 10.1186/1471-2105-9-225.

17.

A network based covariance test for detecting multivariate eQTL in saccharomyces cerevisiae.

Yuan H, Li Z, Tang NL, Deng M.

BMC Syst Biol. 2016 Jan 11;10 Suppl 1:8. doi: 10.1186/s12918-015-0245-0.

18.

Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.

Menéndez P, Kourmpetis YA, ter Braak CJ, van Eeuwijk FA.

PLoS One. 2010 Dec 20;5(12):e14147. doi: 10.1371/journal.pone.0014147.

19.

Construction and analysis of single nucleotide polymorphism-single nucleotide polymorphism interaction networks.

Liu Y, Li X, Liu Z, Chen L, Ng MK.

IET Syst Biol. 2013 Oct;7(5):170-81. doi: 10.1049/iet-syb.2012.0055.

PMID:
24067417

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