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

Khosravi P, Gazestani VH, Pirhaji L, Law B, Sadeghi M, Goliaei B, Bader GD - Algorithms Mol Biol (2015)

Bottom Line: 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.Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these 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.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; The Donnelly Centre, University of Toronto, Toronto, Canada.

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.

No MeSH data available.


Related in: MedlinePlus

Heatmap of selected genes for the in silico network. The heatmap composed of two groups of genes. Genes in each group have similar expression pattern with each other while have opposite pattern with members of the other group. Genes in the red and blue parts are consistently up-regulated and down-regulated, respectively, during cancer progression. These groups of genes were selected based on the prostate cancer expression data.
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Fig2: Heatmap of selected genes for the in silico network. The heatmap composed of two groups of genes. Genes in each group have similar expression pattern with each other while have opposite pattern with members of the other group. Genes in the red and blue parts are consistently up-regulated and down-regulated, respectively, during cancer progression. These groups of genes were selected based on the prostate cancer expression data.

Mentions: To assess the performance of SIREN on a completely known network, we constructed an in silico GRN based on a prostate cancer gene expression data. This GRN is a clique network composed of two groups of genes. Genes in each group have a similar expression pattern to each other and opposite pattern with members of the other group (Figure 2). Because there is a clear expression pattern for each gene, we know the putative interaction types in this network by visual inspection.Figure 2


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

Khosravi P, Gazestani VH, Pirhaji L, Law B, Sadeghi M, Goliaei B, Bader GD - Algorithms Mol Biol (2015)

Heatmap of selected genes for the in silico network. The heatmap composed of two groups of genes. Genes in each group have similar expression pattern with each other while have opposite pattern with members of the other group. Genes in the red and blue parts are consistently up-regulated and down-regulated, respectively, during cancer progression. These groups of genes were selected based on the prostate cancer expression data.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4495944&req=5

Fig2: Heatmap of selected genes for the in silico network. The heatmap composed of two groups of genes. Genes in each group have similar expression pattern with each other while have opposite pattern with members of the other group. Genes in the red and blue parts are consistently up-regulated and down-regulated, respectively, during cancer progression. These groups of genes were selected based on the prostate cancer expression data.
Mentions: To assess the performance of SIREN on a completely known network, we constructed an in silico GRN based on a prostate cancer gene expression data. This GRN is a clique network composed of two groups of genes. Genes in each group have a similar expression pattern to each other and opposite pattern with members of the other group (Figure 2). Because there is a clear expression pattern for each gene, we know the putative interaction types in this network by visual inspection.Figure 2

Bottom Line: 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.Our new method has approximately 68, 70, and 100 percent accuracy, respectively, for these 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.

View Article: PubMed Central - PubMed

Affiliation: School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran ; The Donnelly Centre, University of Toronto, Toronto, Canada.

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.

No MeSH data available.


Related in: MedlinePlus