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

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 accura

SUBMITTER: Chen X 

PROVIDER: S-EPMC2230503 | biostudies-literature | 2007

REPOSITORIES: biostudies-literature