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 c

SUBMITTER: Khosravi P 

PROVIDER: S-EPMC4495944 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature