Inferring interaction type in gene regulatory networks using co-expression data

Springer Science and Business Media LLC - Tập 10 - Trang 1-11 - 2015
Pegah Khosravi1,2, Vahid H Gazestani3, Leila Pirhaji4, Brian Law2,5, Mehdi Sadeghi6,1, Bahram Goliaei7, Gary D Bader2
1School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
2The Donnelly Centre, University of Toronto, Toronto, Canada
3Institute of Parasitology, McGill University, Montreal, Canada
4Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran
5Department of Computer Science, University of Toronto, Toronto, Canada
6National Institute of Genetic Engineering and Biotechnology, Tehran, Iran
7Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran

Tóm tắt

Knowledge of interaction types in biological networks is important for understanding the functional organization of the cell. Currently information-based approaches are widely used for inferring gene regulatory interactions from genomics data, such as gene expression profiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction. This paper describes a novel algorithm, “Signing of Regulatory Networks” (SIREN), which can infer the regulatory type of interactions in a known gene regulatory network (GRN) given corresponding genome-wide gene expression data. To assess our new approach, we applied it to three different benchmark gene regulatory networks, including Escherichia coli, prostate cancer, and an in silico constructed network. Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these networks. To showcase the utility of SIREN algorithm, we used it to predict previously unknown regulation types for 454 interactions related to the prostate cancer GRN. SIREN is an efficient algorithm with low computational complexity; hence, it is applicable to large biological networks. It can serve as a complementary approach for a wide range of network reconstruction methods that do not provide information about the interaction type.

Tài liệu tham khảo

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