Derivation of Transcriptional Regulatory Relationships by Partial Least Squares Regression

As the number of genes in a transcriptional regulatory network is large and the number of samples inbiological data types is usually small, there is a need for integrating multiple data types for reverseengineering these networks. In this paper, we propose a method to integrate microarray gene expression,ChIP-chip and transcription factor binding motif data sets in a partial least squares regression model toderive transcription factors (TFs) -~gene interactions. Both single and synergistic effects of TFs on thepromoters are considered in the model. A method that dynamically updates the significance level based onChIP-chip and binding motif data is proposed. The results evaluated by methods based on Gene Ontologydemonstrate the effectiveness of the proposed approach.

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