Limits...
Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees.

Chen X, Blanchette M - BMC Bioinformatics (2007)

Bottom Line: The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data.Our approach is shown to accurately identify known human liver and erythroid-specific modules.When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

View Article: PubMed Central - HTML - PubMed

Affiliation: McGill Centre for Bioinformatics, 3775 University Street, room 332, Montreal, Quebec, Canada, H3A 2B4. xchen@cs.washington.edu

ABSTRACT

Background: In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression.

Result: We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure.

Conclusion: Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

Show MeSH

Related in: MedlinePlus

The bayesian network used for predicting tissue-specific regulatory modules. See section 'Bayesian network variables' for a description of the variables, and section 'Bayesian network architecture' for a description of their dependencies.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2230503&req=5

Figure 1: The bayesian network used for predicting tissue-specific regulatory modules. See section 'Bayesian network variables' for a description of the variables, and section 'Bayesian network architecture' for a description of their dependencies.

Mentions: Typically, a Bayesian network consists of a set of observed variables, a set of unobserved variables, and an acyclic directed graph describing the direct dependencies between these. In this section, we first introduce the set of variables present in our network, which is depicted in Figure 1. We then describe the dependencies between these variables and the algorithms used to learn the parameters of the network.


Prediction of tissue-specific cis-regulatory modules using Bayesian networks and regression trees.

Chen X, Blanchette M - BMC Bioinformatics (2007)

The bayesian network used for predicting tissue-specific regulatory modules. See section 'Bayesian network variables' for a description of the variables, and section 'Bayesian network architecture' for a description of their dependencies.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2230503&req=5

Figure 1: The bayesian network used for predicting tissue-specific regulatory modules. See section 'Bayesian network variables' for a description of the variables, and section 'Bayesian network architecture' for a description of their dependencies.
Mentions: Typically, a Bayesian network consists of a set of observed variables, a set of unobserved variables, and an acyclic directed graph describing the direct dependencies between these. In this section, we first introduce the set of variables present in our network, which is depicted in Figure 1. We then describe the dependencies between these variables and the algorithms used to learn the parameters of the network.

Bottom Line: The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data.Our approach is shown to accurately identify known human liver and erythroid-specific modules.When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

View Article: PubMed Central - HTML - PubMed

Affiliation: McGill Centre for Bioinformatics, 3775 University Street, room 332, Montreal, Quebec, Canada, H3A 2B4. xchen@cs.washington.edu

ABSTRACT

Background: In vertebrates, a large part of gene transcriptional regulation is operated by cis-regulatory modules. These modules are believed to be regulating much of the tissue-specificity of gene expression.

Result: We develop a Bayesian network approach for identifying cis-regulatory modules likely to regulate tissue-specific expression. The network integrates predicted transcription factor binding site information, transcription factor expression data, and target gene expression data. At its core is a regression tree modeling the effect of combinations of transcription factors bound to a module. A new unsupervised EM-like algorithm is developed to learn the parameters of the network, including the regression tree structure.

Conclusion: Our approach is shown to accurately identify known human liver and erythroid-specific modules. When applied to the prediction of tissue-specific modules in 10 different tissues, the network predicts a number of important transcription factor combinations whose concerted binding is associated to specific expression.

Show MeSH
Related in: MedlinePlus