Email updates

Keep up to date with the latest news and content from BMC Genomics and BioMed Central.

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

For all author emails, please log on.

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

Published: 7 July 2009

Abstract

Background

One of the most challenging tasks in the post-genomic era is to reconstruct the transcriptional regulatory networks. The goal is to reveal, for each gene that responds to a certain biological event, which transcription factors affect its expression, and how a set of transcription factors coordinate to accomplish temporal and spatial specific regulations.

Results

Here we propose a supervised machine learning approach to address these questions. We focus our study on the gene transcriptional regulation of the cell cycle in the budding yeast, thanks to the large amount of data available and relatively well-understood biology, although the main ideas of our method can be applied to other data as well. Our method starts with building an ensemble of decision trees for each microarray data to capture the association between the expression levels of yeast genes and the binding of transcription factors to gene promoter regions, as determined by chromatin immunoprecipitation microarray (ChIP-chip) experiment. Cross-validation experiments show that the method is more accurate and reliable than the naive decision tree algorithm and several other ensemble learning methods. From the decision tree ensembles, we extract logical rules that explain how a set of transcription factors act in concert to regulate the expression of their targets. We further compute a profile for each rule to show its regulation strengths at different time points. We also propose a spline interpolation method to integrate the rule profiles learned from several time series expression data sets that measure the same biological process. We then combine these rule profiles to build a transcriptional regulatory network for the yeast cell cycle. Compared to the results in the literature, our method correctly identifies all major known yeast cell cycle transcription factors, and assigns them into appropriate cell cycle phases. Our method also identifies many interesting synergetic relationships among these transcription factors, most of which are well known, while many of the rest can also be supported by other evidences.

Conclusion

The high accuracy of our method indicates that our method is valid and robust. As more gene expression and transcription factor binding data become available, we believe that our method is useful for reconstructing large-scale transcriptional regulatory networks in other species as well.