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Items: 1 to 20 of 69

1.

Using interpolation to estimate system uncertainty in gene expression experiments.

Falin LJ, Tyler BM.

PLoS One. 2011;6(7):e22071. doi: 10.1371/journal.pone.0022071. Epub 2011 Jul 22.

2.

An integer optimization algorithm for robust identification of non-linear gene regulatory networks.

Chemmangattuvalappil N, Task K, Banerjee I.

BMC Syst Biol. 2012 Sep 2;6:119.

3.

Mining gene expression data by interpreting principal components.

Roden JC, King BW, Trout D, Mortazavi A, Wold BJ, Hart CE.

BMC Bioinformatics. 2006 Apr 7;7:194.

4.

Supervised inference of gene regulatory networks from positive and unlabeled examples.

Mordelet F, Vert JP.

Methods Mol Biol. 2013;939:47-58. doi: 10.1007/978-1-62703-107-3_5.

PMID:
23192540
5.

A reliable measure of similarity based on dependency for short time series: an application to gene expression networks.

Campiteli MG, Soriani FM, Malavazi I, Kinouchi O, Pereira CA, Goldman GH.

BMC Bioinformatics. 2009 Aug 28;10:270. doi: 10.1186/1471-2105-10-270.

6.

Inferring large-scale gene regulatory networks using a low-order constraint-based algorithm.

Wang M, Augusto Benedito V, Xuechun Zhao P, Udvardi M.

Mol Biosyst. 2010 Jun;6(6):988-98. doi: 10.1039/b917571g. Epub 2010 Feb 19.

PMID:
20485743
7.

svdPPCS: an effective singular value decomposition-based method for conserved and divergent co-expression gene module identification.

Zhang W, Edwards A, Fan W, Zhu D, Zhang K.

BMC Bioinformatics. 2010 Jun 22;11:338. doi: 10.1186/1471-2105-11-338.

8.

Spectral estimation in unevenly sampled space of periodically expressed microarray time series data.

Liew AW, Xian J, Wu S, Smith D, Yan H.

BMC Bioinformatics. 2007 Apr 24;8:137.

9.

Identifying differential correlation in gene/pathway combinations.

Braun R, Cope L, Parmigiani G.

BMC Bioinformatics. 2008 Nov 18;9:488. doi: 10.1186/1471-2105-9-488.

10.
11.

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.

12.

Improvements in the reconstruction of time-varying gene regulatory networks: dynamic programming and regularization by information sharing among genes.

Grzegorczyk M, Husmeier D.

Bioinformatics. 2011 Mar 1;27(5):693-9. doi: 10.1093/bioinformatics/btq711. Epub 2010 Dec 21.

PMID:
21177328
13.

Functional genomics and proteomics in the clinical neurosciences: data mining and bioinformatics.

Phan JH, Quo CF, Wang MD.

Prog Brain Res. 2006;158:83-108. Review.

PMID:
17027692
14.

A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology.

Grzegorczyk M, Husmeier D.

Stat Appl Genet Mol Biol. 2012 Jul 12;11(4). pii: /j/sagmb.2012.11.issue-4/1544-6115.1761/1544-6115.1761.xml. doi: 10.1515/1544-6115.1761.

PMID:
22850067
16.

Clustering short time series gene expression data.

Ernst J, Nau GJ, Bar-Joseph Z.

Bioinformatics. 2005 Jun;21 Suppl 1:i159-68.

PMID:
15961453
17.

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.

18.

Learning gene regulatory networks from gene expression measurements using non-parametric molecular kinetics.

Aijö T, Lähdesmäki H.

Bioinformatics. 2009 Nov 15;25(22):2937-44. doi: 10.1093/bioinformatics/btp511. Epub 2009 Aug 25.

PMID:
19706742
19.

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.

20.

Discovery of Time-Delayed Gene Regulatory Networks based on temporal gene expression profiling.

Li X, Rao S, Jiang W, Li C, Xiao Y, Guo Z, Zhang Q, Wang L, Du L, Li J, Li L, Zhang T, Wang QK.

BMC Bioinformatics. 2006 Jan 18;7:26.

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