Derivation of Transcriptional Regulatory Relationships by Partial Least Squares Regression

Date

2009-11-04

Author

Tan, Mehmet
Polat, Faruk
Alhajj, Reda

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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.

Subject Keywords

Least squares methods, Biomedical engineering, Biological system modeling, Bioinformatics, Biology computing, Computer science, Data mining, Covariance matrix, Biomedical computing, Gene expression

URI

https://hdl.handle.net/11511/37601

DOI

https://doi.org/10.1109/bibm.2009.62

Conference Name

IEEE International Conference on Bioinformatics and Biomedicine (BIBMW 2009)

Collections

Department of Computer Engineering, Conference / Seminar

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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: https://hdl.handle.net/11511/37601.