Derivation of Transcriptional Regulatory Relationships by Partial Least Squares Regression

Tan, Mehmet
Polat, Faruk
Alhajj, Reda
As the number of genes in a transcriptional regulatory network is large and the number of samples in biological data types is usually small, there is a need for integrating multiple data types for reverse engineering 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 to derive transcription factors (TFs) gene interactions. Both single and synergistic effects of TFs on the promoters are considered in the model. A method that dynamically updates the significance level based on ChIP-chip and binding motif data is proposed. The results evaluated by methods based on Gene Ontology demonstrate the effectiveness of the proposed approach.