title: Inferring interaction type in gene regulatory networks using co-expression data.
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, 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 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.
authors.affiliation: School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.|The Donnelly Centre, University of Toronto, Toronto, Canada.
authors.forename: Pegah
authors.initials: P
authors.lastname: Khosravi
authors.affiliation: Institute of Parasitology, McGill University, Montreal, QC Canada.
authors.forename: Vahid H
authors.initials: VH
authors.lastname: Gazestani
authors.affiliation: Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
authors.forename: Leila
authors.initials: L
authors.lastname: Pirhaji
authors.affiliation: The Donnelly Centre, University of Toronto, Toronto, Canada.|Department of Computer Science, University of Toronto, Toronto, Canada.
authors.forename: Brian
authors.initials: B
authors.lastname: Law
authors.affiliation: National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.|School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.
authors.forename: Mehdi
authors.initials: M
authors.lastname: Sadeghi
authors.affiliation: Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran.
authors.forename: Bahram
authors.initials: B
authors.lastname: Goliaei
authors.affiliation: The Donnelly Centre, University of Toronto, Toronto, Canada.
authors.forename: Gary D
authors.initials: GD
authors.lastname: Bader
keywords: Gene expression data; Information-based approach; Interaction type; Regulatory interaction