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Items: 1 to 20 of 215

1.

Clustering analysis of SAGE transcription profiles using a Poisson approach.

Huang H, Cai L, Wong WH.

Methods Mol Biol. 2008;387:185-98.

PMID:
18287632
2.

A Poisson-based adaptive affinity propagation clustering for SAGE data.

Tang D, Zhu Q, Yang F.

Comput Biol Chem. 2010 Feb;34(1):63-70. doi: 10.1016/j.compbiolchem.2009.11.001.

PMID:
20042369
4.

Clustering analysis of SAGE data using a Poisson approach.

Cai L, Huang H, Blackshaw S, Liu JS, Cepko C, Wong WH.

Genome Biol. 2004;5(7):R51.

5.

POWER_SAGE: comparing statistical tests for SAGE experiments.

Man MZ, Wang X, Wang Y.

Bioinformatics. 2000 Nov;16(11):953-9.

6.

Distance-based clustering of CGH data.

Liu J, Mohammed J, Carter J, Ranka S, Kahveci T, Baudis M.

Bioinformatics. 2006 Aug 15;22(16):1971-8.

7.
8.

Modeling Sage data with a truncated gamma-Poisson model.

Thygesen HH, Zwinderman AH.

BMC Bioinformatics. 2006 Mar 20;7:157.

9.

Identifying nonspecific SAGE tags by context of gene expression.

Ge X, Wang SM.

Methods Mol Biol. 2008;387:199-204.

PMID:
18287633
10.

Statistical modeling of sequencing errors in SAGE libraries.

Beissbarth T, Hyde L, Smyth GK, Job C, Boon WM, Tan SS, Scott HS, Speed TP.

Bioinformatics. 2004 Aug 4;20 Suppl 1:i31-9.

11.

PathCluster: a framework for gene set-based hierarchical clustering.

Kim TM, Yim SH, Jeong YB, Jung YC, Chung YJ.

Bioinformatics. 2008 Sep 1;24(17):1957-8. doi: 10.1093/bioinformatics/btn357.

12.

Computing the maximum similarity bi-clusters of gene expression data.

Liu X, Wang L.

Bioinformatics. 2007 Jan 1;23(1):50-6.

13.

Analysis of expression profile using fuzzy adaptive resonance theory.

Tomida S, Hanai T, Honda H, Kobayashi T.

Bioinformatics. 2002 Aug;18(8):1073-83.

14.

Moderated statistical tests for assessing differences in tag abundance.

Robinson MD, Smyth GK.

Bioinformatics. 2007 Nov 1;23(21):2881-7.

15.

Statistical comparison of two or more SAGE libraries: one tag at a time.

Schaaf GJ, van Ruissen F, van Kampen A, Kool M, Ruijter JM.

Methods Mol Biol. 2008;387:151-68.

PMID:
18287630
16.
17.

A seriation approach for visualization-driven discovery of co-expression patterns in Serial Analysis of Gene Expression (SAGE) data.

Morozova O, Morozov V, Hoffman BG, Helgason CD, Marra MA.

PLoS One. 2008 Sep 12;3(9):e3205. doi: 10.1371/journal.pone.0003205.

18.

USAGE: a web-based approach towards the analysis of SAGE data. Serial Analysis of Gene Expression.

van Kampen AH, van Schaik BD, Pauws E, Michiels EM, Ruijter JM, Caron HN, Versteeg R, Heisterkamp SH, Leunissen JA, Baas F, van der Mee M.

Bioinformatics. 2000 Oct;16(10):899-905.

19.

Improving pattern discovery and visualization of SAGE data through poisson-based self-adaptive neural networks.

Zheng H, Wang H, Azuaje F.

IEEE Trans Inf Technol Biomed. 2008 Jul;12(4):459-69. doi: 10.1109/TITB.2007.901208.

PMID:
18632326
20.

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.

PMID:
18289772
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