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Predicting Eukaryotic Transcriptional Cooperativity by Bayesian Network Integration of Genome-Wide Data

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dc.contributor.author Wang, Yong en_US
dc.contributor.author Zhang, Xiang-Sun en_US
dc.contributor.author Xia, Yu en_US
dc.date.accessioned 2012-01-11T21:10:57Z
dc.date.available 2012-01-11T21:10:57Z
dc.date.copyright 2009 en_US
dc.date.issued 2009-10 en_US
dc.identifier.citation Wang, Yong, Xiang-Sun Zhang, Yu Xia. "Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data" 37(18): 5943-5958. (2009) en_US
dc.identifier.issn 1362-4962 en_US
dc.identifier.uri http://hdl.handle.net/2144/3207
dc.description.abstract Transcriptional cooperativity among several transcription factors (TFs) is believed to be the main mechanism of complexity and precision in transcriptional regulatory programs. Here, we present a Bayesian network framework to reconstruct a high-confidence whole-genome map of transcriptional cooperativity in Saccharomyces cerevisiae by integrating a comprehensive list of 15 genomic features. We design a Bayesian network structure to capture the dominant correlations among features and TF cooperativity, and introduce a supervised learning framework with a well-constructed gold-standard dataset. This framework allows us to assess the predictive power of each genomic feature, validate the superior performance of our Bayesian network compared to alternative methods, and integrate genomic features for optimal TF cooperativity prediction. Data integration reveals 159 high-confidence predicted cooperative relationships among 105 TFs, most of which are subsequently validated by literature search. The existing and predicted transcriptional cooperativities can be grouped into three categories based on the combination patterns of the genomic features, providing further biological insights into the different types of TF cooperativity. Our methodology is the first supervised learning approach for predicting transcriptional cooperativity, compares favorably to alternative unsupervised methodologies, and can be applied to other genomic data integration tasks where high-quality gold-standard positive data are scarce. en_US
dc.description.sponsorship National Natural Science Foundation of China (10801131); Chinese Academy of Sciences (kjcs-yw-s7); National Basic Research Program (2006CB503900); National Natural Science Foundation of China (10631070, 60873205); PhRMA Foundation en_US
dc.language.iso en en_US
dc.rights Copyright 2009 Wang, Yong, Xiang-Sun Zhang, Yu Xia en_US
dc.rights.uri http://creativecommons.org/licenses/by-nc/2.0/uk/ en_US
dc.title Predicting Eukaryotic Transcriptional Cooperativity by Bayesian Network Integration of Genome-Wide Data en_US
dc.type article en_US
dc.identifier.doi 10.1093/nar/gkp625 en_US
dc.identifier.pubmedid 19661283 en_US
dc.identifier.pmcid 2764433 en_US


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Copyright 2009 Wang, Yong, Xiang-Sun Zhang, Yu Xia Except where otherwise noted, this item's license is described as Copyright 2009 Wang, Yong, Xiang-Sun Zhang, Yu Xia

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