Limits...
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

Selecting the best threshold for SIREN. The relationship between accuracy and retrieval and the best threshold with highest accuracy and fair retrieval. A accuracy, R retrieval; EE. coli, P prostate, S in silico. As the figure shows in ±0.158, no interaction type was detected randomly by SIREN where the color spectrums were changed to be completely white in accuracy and retrieval of all three random networks.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig8: Selecting the best threshold for SIREN. The relationship between accuracy and retrieval and the best threshold with highest accuracy and fair retrieval. A accuracy, R retrieval; EE. coli, P prostate, S in silico. As the figure shows in ±0.158, no interaction type was detected randomly by SIREN where the color spectrums were changed to be completely white in accuracy and retrieval of all three random networks.

Mentions: To select the best threshold on the resulting SIREN score, we applied it to the E. coli, prostate and in silico benchmarks, using the S1 scoring function and the M3 rescaling matrix. We tested a range of different cut-off scores (20 different thresholds between 0 and 1) for SIREN to determine a reliable threshold for various networks. The results showed that when the cut-off threshold is greater than +0.158 or smaller than −0.158, SIREN does not detect any interaction type in random data (generated by 106 times of shuffling gene expression data) (Figure 8), while many interactions are predicted to be signed in the benchmark GRNs.Figure 8


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)

Selecting the best threshold for SIREN. The relationship between accuracy and retrieval and the best threshold with highest accuracy and fair retrieval. A accuracy, R retrieval; EE. coli, P prostate, S in silico. As the figure shows in ±0.158, no interaction type was detected randomly by SIREN where the color spectrums were changed to be completely white in accuracy and retrieval of all three random networks.
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig8: Selecting the best threshold for SIREN. The relationship between accuracy and retrieval and the best threshold with highest accuracy and fair retrieval. A accuracy, R retrieval; EE. coli, P prostate, S in silico. As the figure shows in ±0.158, no interaction type was detected randomly by SIREN where the color spectrums were changed to be completely white in accuracy and retrieval of all three random networks.
Mentions: To select the best threshold on the resulting SIREN score, we applied it to the E. coli, prostate and in silico benchmarks, using the S1 scoring function and the M3 rescaling matrix. We tested a range of different cut-off scores (20 different thresholds between 0 and 1) for SIREN to determine a reliable threshold for various networks. The results showed that when the cut-off threshold is greater than +0.158 or smaller than −0.158, SIREN does not detect any interaction type in random data (generated by 106 times of shuffling gene expression data) (Figure 8), while many interactions are predicted to be signed in the benchmark GRNs.Figure 8

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