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Paper   IPM / Biological / 13968
School of Biological Sciences
  Title:   Inferring interaction type in gene regulatory networks using co-expression data
1.  P. Khosravi.
2.  V. H. Gazestani.
3.  L. Pirhaji.
4.  B. Law.
5.  M. Sadeghi.
6.  B. Goliaei.
7.  G. D. Bader.
  Status:   Published
  Journal: AMB
  No.:  1
  Vol.:  Vol: 10, DOI: 10.1186/s13015-015-0054-4
  Year:  2015
  Pages:   23
  Supported by:  IPM
Background: 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 interac- tions from genomics data, such as gene expression proiles; however, these approaches do not provide evidence about the regulation type (positive or negative sign) of the interaction. Results: 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 diferent 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. Conclusions: SIREN is an eicient algorithm with low computational complexity; hence, it is applicable to large bio- logical 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. Keywords: Gene expression data, Information-based approach, Interaction type, Regulatory interaction

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