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.
Citation Formats
M. Tan, F. Polat, and R. Alhajj, “Derivation of Transcriptional Regulatory Relationships by Partial Least Squares Regression,” presented at the IEEE International Conference on Bioinformatics and Biomedicine (BIBMW 2009), Washington, DC, 2009, Accessed: 00, 2020. [Online]. Available: