1-1-2009 An Ensemble Learning Approach to Reverse-Engineering Transcriptional Regulatory Networks from Time-Series Gene Expression Data

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. Crossvalidation 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 from The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08) Las Vegas, NV, USA. 14–17 July 2008 Published: 7 July 2009 BMC Genomics 2009, 10(Suppl 1):S8 doi:10.1186/1471-2164-10-S1-S8 <p>The 2008 International Conference on Bioinformatics & Computational Biology (BIOCOMP'08)</p> Youping Deng, Mary Qu Yang, Hamid R Arabnia, and Jack Y Yang Publication of this supplement was made possible with support from the International Society of Intelligent Biological Medicine (ISIBM). Research http://www.biomedcentral. com/content/pdf/1471-2164-10-S1-info.pdf This article is available from: http://www.biomedcentral. com/1471-2164/10/S1/S8 © 2009 Ruan et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. BMC Genomics 2009, 10(Suppl 1):S8 http://www.biomedcentral. com/1471-2164/10/S1/S8