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Items: 1 to 20 of 53

1.
2.

Detecting biological associations between genes based on the theory of phase synchronization.

Kim CS, Riikonen P, Salakoski T.

Biosystems. 2008 May;92(2):99-113. doi: 10.1016/j.biosystems.2007.12.006. Epub 2008 Jan 11.

PMID:
18289772
3.

Merging microarray cell synchronization experiments through curve alignment.

Hermans F, Tsiporkova E.

Bioinformatics. 2007 Jan 15;23(2):e64-70.

PMID:
17237107
4.

Identifying cycling genes by combining sequence homology and expression data.

Lu Y, Rosenfeld R, Bar-Joseph Z.

Bioinformatics. 2006 Jul 15;22(14):e314-22.

PMID:
16873488
5.

Bayesian detection of periodic mRNA time profiles without use of training examples.

Andersson CR, Isaksson A, Gustafsson MG.

BMC Bioinformatics. 2006 Feb 9;7:63.

6.

Combined static and dynamic analysis for determining the quality of time-series expression profiles.

Simon I, Siegfried Z, Ernst J, Bar-Joseph Z.

Nat Biotechnol. 2005 Dec;23(12):1503-8.

PMID:
16333294
7.

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.

8.

Fusing microarray experiments with multivariate regression.

Gilks WR, Tom BD, Brazma A.

Bioinformatics. 2005 Sep 1;21 Suppl 2:ii137-43.

PMID:
16204093
9.

Polynomial model approach for resynchronization analysis of cell-cycle gene expression data.

Qiu P, Wang ZJ, Liu KJ.

Bioinformatics. 2006 Apr 15;22(8):959-66. Epub 2006 Jan 24.

PMID:
16434439
10.

New weakly expressed cell cycle-regulated genes in yeast.

de Lichtenberg U, Wernersson R, Jensen TS, Nielsen HB, Fausbøll A, Schmidt P, Hansen FB, Knudsen S, Brunak S.

Yeast. 2005 Nov;22(15):1191-201.

11.

MARD: a new method to detect differential gene expression in treatment-control time courses.

Cheng C, Ma X, Yan X, Sun F, Li LM.

Bioinformatics. 2006 Nov 1;22(21):2650-7. Epub 2006 Aug 23.

PMID:
16928738
12.

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.

13.

The Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data.

Mansourian R, Mutch DM, Antille N, Aubert J, Fogel P, Le Goff JM, Moulin J, Petrov A, Rytz A, Voegel JJ, Roberts MA.

Bioinformatics. 2004 Nov 1;20(16):2726-37. Epub 2004 May 14.

PMID:
15145801
14.

Are we overestimating the number of cell-cycling genes? The impact of background models on time-series analysis.

Futschik ME, Herzel H.

Bioinformatics. 2008 Apr 15;24(8):1063-9. doi: 10.1093/bioinformatics/btn072. Epub 2008 Feb 29.

PMID:
18310054
15.

A theoretical analysis of the selection of differentially expressed genes.

Mukherjee S, Roberts SJ.

J Bioinform Comput Biol. 2005 Jun;3(3):627-43.

PMID:
16108087
16.

A method for clustering gene expression data based on graph structure.

Seno S, Teramoto R, Takenaka Y, Matsuda H.

Genome Inform. 2004;15(2):151-60.

PMID:
15706501
17.

CLEAR-test: combining inference for differential expression and variability in microarray data analysis.

Valls J, Grau M, Solé X, Hernández P, Montaner D, Dopazo J, Peinado MA, Capellá G, Moreno V, Pujana MA.

J Biomed Inform. 2008 Feb;41(1):33-45. Epub 2007 May 17.

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20.

Extensions to gene set enrichment.

Jiang Z, Gentleman R.

Bioinformatics. 2007 Feb 1;23(3):306-13. Epub 2006 Nov 24.

PMID:
17127676

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