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Items: 1 to 20 of 404

1.

Fitting Boolean networks from steady state perturbation data.

Almudevar A, McCall MN, McMurray H, Land H.

Stat Appl Genet Mol Biol. 2011 Oct 5;10(1). pii: /j/sagmb.2011.10.issue-1/1544-6115.1727/1544-6115.1727.xml. doi: 10.2202/1544-6115.1727.

2.

A full bayesian approach for boolean genetic network inference.

Han S, Wong RK, Lee TC, Shen L, Li SY, Fan X.

PLoS One. 2014 Dec 31;9(12):e115806. doi: 10.1371/journal.pone.0115806. eCollection 2014.

3.

Simulation study in Probabilistic Boolean Network models for genetic regulatory networks.

Zhang SQ, Ching WK, Ng MK, Akutsu T.

Int J Data Min Bioinform. 2007;1(3):217-40.

PMID:
18399072
4.

An approximation method for solving the steady-state probability distribution of probabilistic Boolean networks.

Ching WK, Zhang S, Ng MK, Akutsu T.

Bioinformatics. 2007 Jun 15;23(12):1511-8. Epub 2007 Apr 26.

PMID:
17463027
5.

Using complexity for the estimation of Bayesian networks.

Salzman P, Almudevar A.

Stat Appl Genet Mol Biol. 2006;5:Article21. Epub 2006 Aug 31.

PMID:
17049032
6.

A review on the computational approaches for gene regulatory network construction.

Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z.

Comput Biol Med. 2014 May;48:55-65. doi: 10.1016/j.compbiomed.2014.02.011. Epub 2014 Feb 24. Review.

PMID:
24637147
7.

An information theoretic approach to pedigree reconstruction.

Almudevar A.

Theor Popul Biol. 2016 Feb;107:52-64. doi: 10.1016/j.tpb.2015.09.006. Epub 2015 Oct 8.

PMID:
26453931
8.
9.

Joint estimation of causal effects from observational and intervention gene expression data.

Rau A, Jaffrézic F, Nuel G.

BMC Syst Biol. 2013 Oct 31;7:111. doi: 10.1186/1752-0509-7-111.

10.

Integrating Bayesian variable selection with Modular Response Analysis to infer biochemical network topology.

Santra T, Kolch W, Kholodenko BN.

BMC Syst Biol. 2013 Jul 6;7:57. doi: 10.1186/1752-0509-7-57.

11.

Bayesian probabilistic network modeling from multiple independent replicates.

Patton KL, John DJ, Norris JL.

BMC Bioinformatics. 2012 Jun 11;13 Suppl 9:S6. doi: 10.1186/1471-2105-13-S9-S6.

12.

Weighted lasso in graphical Gaussian modeling for large gene network estimation based on microarray data.

Shimamura T, Imoto S, Yamaguchi R, Miyano S.

Genome Inform. 2007;19:142-53.

PMID:
18546512
13.

An experimental design framework for Markovian gene regulatory networks under stationary control policy.

Dehghannasiri R, Shahrokh Esfahani M, Dougherty ER.

BMC Syst Biol. 2018 Dec 21;12(Suppl 8):137. doi: 10.1186/s12918-018-0649-8.

14.

Gene expression complex networks: synthesis, identification, and analysis.

Lopes FM, Cesar RM, Costa Lda F.

J Comput Biol. 2011 Oct;18(10):1353-67. doi: 10.1089/cmb.2010.0118. Epub 2011 May 6.

PMID:
21548810
15.

A comparison of Monte Carlo-based Bayesian parameter estimation methods for stochastic models of genetic networks.

Mariño IP, Zaikin A, Míguez J.

PLoS One. 2017 Aug 10;12(8):e0182015. doi: 10.1371/journal.pone.0182015. eCollection 2017.

16.

Stochastic Boolean networks: an efficient approach to modeling gene regulatory networks.

Liang J, Han J.

BMC Syst Biol. 2012 Aug 28;6:113. doi: 10.1186/1752-0509-6-113.

17.

Boolean regulatory network reconstruction using literature based knowledge with a genetic algorithm optimization method.

Dorier J, Crespo I, Niknejad A, Liechti R, Ebeling M, Xenarios I.

BMC Bioinformatics. 2016 Oct 6;17(1):410.

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.

Bayesian network reconstruction using systems genetics data: comparison of MCMC methods.

Tasaki S, Sauerwine B, Hoff B, Toyoshiba H, Gaiteri C, Chaibub Neto E.

Genetics. 2015 Apr;199(4):973-89. doi: 10.1534/genetics.114.172619. Epub 2015 Jan 28.

20.

Inference of regulatory networks with a convergence improved MCMC sampler.

Agostinho NB, Machado KS, Werhli AV.

BMC Bioinformatics. 2015 Sep 24;16:306. doi: 10.1186/s12859-015-0734-6.

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