Inferring interaction type in gene regulatory networks using co-expression data. Issue 1 (December 2015)

• Record Type: Journal Article
• Authors: Khosravi, Pegah; Gazestani, Vahid; Pirhaji, Leila; Law, Brian; Sadeghi, Mehdi; Goliaei, Bahram; Bader, Gary
• Is Part Of: Algorithms for molecular biology. Volume 10:Issue 1(2015)
• Page Start: 1
• Page End: 11
• Publication Date: 2015-12
• Subjects: Gene expression data -- Information-based approach -- Interaction type -- Regulatory interaction; Molecular biology -- Mathematical models -- Periodicals; Algorithms -- Periodicals; Bioinformatics -- Periodicals; 572.8015118
• DOI: 10.1186/s13015-015-0054-4
• Languages: English
• ISSNs: 1748-7188

Abstract

Abstract 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 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. 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 different benchmark gene regulatory networks, includingEscherichia 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 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.