Bayesian hierarchical model for transcriptional module discovery by jointly modeling gene expression and ChIP-chip data
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* Corresponding author: Mario Medvedovic Mario.Medvedovic@uc.edu
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
BMC Bioinformatics 2007, 8:283 doi:10.1186/1471-2105-8-283
Published: 3 August 2007Additional files
Additional file 1:
Liu-et-al-TranscriptionalModuleDiscovery-WebSupplement4.doc. Additional data analysis results and remaining prior and posterior conditional probability distribution formulas.
Format: XLS Size: 17KB Download file
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Additional file 2:
SupplementalTable1_GRAM_parameters.xls. lists of GRAM's parameters and corresponding true and false positive rates.
Format: XLS Size: 16KB Download file
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Additional file 3:
SupplementalTable2_SAMBA_parameters.xls. lists of SAMBA's parameters and corresponding true and false positive rates.
Format: XLS Size: 35KB Download file
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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.
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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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