Combining Multiple Types of Biological Data in Constraint-Based Learning of Gene Regulatory Networks

Date

2008-09-17

Author

Tan, Mehmet
AlShalalfa, Mohammed
Alhajj, Reda
Polat, Faruk

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Due to the complex structure and scale of gene regulatory networks, we support the argument that combination of multiple types of biological data to derive satisfactory network structures is necessary to understand the regulatory mechanisms of cellular systems. In this paper, we propose a simple but effective method of combining two types of biological data, namely microarray and transcription factor (TF) binding data, to construct gene regulatory networks. The proposed algorithm is based on and extends the well-known PC algorithm [23]. Further, we developed a method for measuring the significance of the interactions between the genes and the TFs. The reported test results on both synthetic and real data sets demonstrate the applicability and effectiveness of the proposed approach; we also report the results of some comparative analysis that highlights the power of the proposed approach.

Subject Keywords

Bayesian networks, Discovery

URI

https://hdl.handle.net/11511/45686

DOI

https://doi.org/10.1109/cibcb.2008.4675764

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

M. Tan, M. AlShalalfa, R. Alhajj, and F. Polat, “Combining Multiple Types of Biological Data in Constraint-Based Learning of Gene Regulatory Networks,” 2008, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/45686.