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This article is part of the supplement: The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08)

Open Access Research

An ensemble learning approach to reverse-engineering transcriptional regulatory networks from time-series gene expression data

Jianhua Ruan1*, Youping Deng23, Edward J Perkins4 and Weixiong Zhang56*

Author Affiliations

1 Department of Computer Science, The University of Texas at San Antonio, San Antonio, TX 78249, USA

2 SpecPro Inc., Vicksburg, MS 39180, USA

3 Department of Biological Sciences, University of Southern Mississippi, Hattiesburg, MS 39406, USA

4 Environmental Laboratory, U.S. Army Engineer Research and Development Center, Vicksburg, MS 39180, USA

5 Department of Computer Science and Engineering, Washington University in St Louis, St Louis, MO 63130, USA

6 Department of Genetics, Washington University School of Medicine, St Louis, MO 63110, USA

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BMC Genomics 2009, 10(Suppl 1):S8  doi:10.1186/1471-2164-10-S1-S8

Published: 7 July 2009

Additional files

Additional file 1:

This PDF file contains all the significant regulatory rules learned from the CDC28 data set using the ensemble approach.

Format: PDF Size: 36KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 2:

This PDF file contains all the significant regulatory rules learned from the α-factor data set using the ensemble approach.

Format: PDF Size: 40KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 3:

This PDF file contains all the significant regulatory rules learned from the CDC15 data set using the ensemble approach.

Format: PDF Size: 106KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 4:

This PDF file contains the integrated rule profiles that show a cell-cycle dependency.

Format: PDF Size: 293KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data

Additional file 5:

This PDF file contains the integrated rule profiles that do not show a clear cell-cycle dependency.

Format: PDF Size: 58KB Download file

This file can be viewed with: Adobe Acrobat Reader

Open Data