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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

Four rescaling matrices. The design of four rescaling matrices evaluated for use in SIREN.
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Fig6: Four rescaling matrices. The design of four rescaling matrices evaluated for use in SIREN.

Mentions: We have used a rescaling matrix to convert the MI function, which normally generates a non-negative score, to a function that can produce negative values. The resulting sign is used to predict either an activating (similar expression profiles) or inhibitory effect (different expression profiles) between genes. Using the B-spline approach, we smoothly discretized the expression profile of each gene into 10 bins. For each interacting pair, SIREN creates a two-dimensional grid with 100 cells. The distribution pattern of expression data in these 100 cells is used for predicting the interaction type. The interaction type can be inferred from this grid because the distribution pattern for genes with positively correlated expression patterns will be different from the distribution pattern of genes with a negative association. To discriminate the distribution patterns from each other, we have introduced the rescaling matrix (Figure 6). The design of four rescaling matrices evaluated for use in SIREN. For all the matrices, we initially assigned −1 to the two most negative bins and +1 to the most positive bins, on the diagonal of the matrix. We also arranged the matrices to each have equal number of positive and negative cells (42 positive, 42 negative, and 16 zero cells).Figure 6


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)

Four rescaling matrices. The design of four rescaling matrices evaluated for use in SIREN.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig6: Four rescaling matrices. The design of four rescaling matrices evaluated for use in SIREN.
Mentions: We have used a rescaling matrix to convert the MI function, which normally generates a non-negative score, to a function that can produce negative values. The resulting sign is used to predict either an activating (similar expression profiles) or inhibitory effect (different expression profiles) between genes. Using the B-spline approach, we smoothly discretized the expression profile of each gene into 10 bins. For each interacting pair, SIREN creates a two-dimensional grid with 100 cells. The distribution pattern of expression data in these 100 cells is used for predicting the interaction type. The interaction type can be inferred from this grid because the distribution pattern for genes with positively correlated expression patterns will be different from the distribution pattern of genes with a negative association. To discriminate the distribution patterns from each other, we have introduced the rescaling matrix (Figure 6). The design of four rescaling matrices evaluated for use in SIREN. For all the matrices, we initially assigned −1 to the two most negative bins and +1 to the most positive bins, on the diagonal of the matrix. We also arranged the matrices to each have equal number of positive and negative cells (42 positive, 42 negative, and 16 zero cells).Figure 6

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