BMC Bioinformatics

official impact factor 3.03

Open Access Highly Access Research article

Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data

Xiangdong Liu1,2, Walter J Jessen2, Siva Sivaganesan3, Bruce J Aronow2 and Mario Medvedovic1,2*

Author Affiliations

1 Department of Environmental Health, University of Cincinnati, 3223 Eden Ave. ML 56, Cincinnati, Ohio 45267, USA

2 Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio 45229, USA

3 Mathematical Sciences Department, University of Cincinnati, Cincinnati, OH 45221, USA

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BMC Bioinformatics 2007, 8:283 doi:10.1186/1471-2105-8-283

Published: 3 August 2007

Additional files

Additional file 1:

Liu-et-al-TranscriptionalModuleDiscovery-WebSupplement4.doc. Additional data analysis results and remaining prior and posterior conditional probability distribution formulas.

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Additional file 2:

SupplementalTable1_GRAM_parameters.xls. lists of GRAM's parameters and corresponding true and false positive rates.

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Additional file 3:

SupplementalTable2_SAMBA_parameters.xls. lists of SAMBA's parameters and corresponding true and false positive rates.

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Additional file 4:

SupplementalTable3_GRAM_ECIM_GO.xls. The comparison of TMs generated by GRAM and ECIM using the Sporulation-CellCycle expression datasets used in Figures 2.

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Additional file 5:

SupplementalTable4_TMs.xls. Details of all Sporulation-CellCycle modules depicted in Figures 7 and 8B in the main text.

Format: XLS Size: 75KB Download file

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Additional file 6:

SupplementalTable5_TMs_Harbinson.xls. Details of all Sporulation-CellCycle modules Identified using Sporulation-CellCycle dataset and the Harbinson ChIP-chip dataset[60].

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