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Validation of gene regulatory network inference based on controllability.

Qian X, Dougherty ER - Front Genet (2013)

Bottom Line: The reasoning behind such a criterion is that, if our purpose is to use gene regulatory networks to design therapeutic intervention strategies, then we are not concerned with network fidelity, per se, but only with our ability to design effective interventions based on the inferred network.The objective of a control policy is to optimally reduce the total steady-state probability mass of the undesirable states (phenotypes), which is equivalent to optimally increasing the total steady-state mass of the desirable states.Hence, when one is aiming at a specific application, it may be wise to use an objective-based measure of inference validity.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Texas A & M University TX, USA.

ABSTRACT

There are two distinct issues regarding network validation: (1) Does an inferred network provide good predictions relative to experimental data? (2) Does a network inference algorithm applied within a certain network model framework yield networks that are accurate relative to some criterion of goodness? The first issue concerns scientific validation and the second concerns algorithm validation. In this paper we consider inferential validation relative to controllability; that is, if an inference procedure is applied to data generated from a gene regulatory network and an intervention procedure is designed on the inferred network, how well does it perform on the true network? The reasoning behind such a criterion is that, if our purpose is to use gene regulatory networks to design therapeutic intervention strategies, then we are not concerned with network fidelity, per se, but only with our ability to design effective interventions based on the inferred network. We will consider the problem from the perspectives of stationary control, which involves designing a control policy to be applied over time based on the current state of the network, with the decision procedure itself being time independent. The objective of a control policy is to optimally reduce the total steady-state probability mass of the undesirable states (phenotypes), which is equivalent to optimally increasing the total steady-state mass of the desirable states. Based on this criterion we compare several proposed network inference procedures. We will see that inference procedure ψ may perform poorer than inference procedure ξ relative to inferring the full network structure but perform better than ξ relative to controllability. Hence, when one is aiming at a specific application, it may be wise to use an objective-based measure of inference validity.

No MeSH data available.


Comparison of five network inference algorithms by normalized false positive rates. (A) BNps with 7 genes and K = 3; (B) BNps with 7 genes and K = 5; (C) BNps with 9 genes and K = 3; (D) BNps with 9 genes and K = 5.
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FA1: Comparison of five network inference algorithms by normalized false positive rates. (A) BNps with 7 genes and K = 3; (B) BNps with 7 genes and K = 5; (C) BNps with 9 genes and K = 3; (D) BNps with 9 genes and K = 5.

Mentions: We plot the normalized false positive rates (the ratio of the number of false positive regulators over the total number of edges) in Figure A1, in which we can see that the performance of different algorithms are consistent as we discussed previously.


Validation of gene regulatory network inference based on controllability.

Qian X, Dougherty ER - Front Genet (2013)

Comparison of five network inference algorithms by normalized false positive rates. (A) BNps with 7 genes and K = 3; (B) BNps with 7 genes and K = 5; (C) BNps with 9 genes and K = 3; (D) BNps with 9 genes and K = 5.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3860259&req=5

FA1: Comparison of five network inference algorithms by normalized false positive rates. (A) BNps with 7 genes and K = 3; (B) BNps with 7 genes and K = 5; (C) BNps with 9 genes and K = 3; (D) BNps with 9 genes and K = 5.
Mentions: We plot the normalized false positive rates (the ratio of the number of false positive regulators over the total number of edges) in Figure A1, in which we can see that the performance of different algorithms are consistent as we discussed previously.

Bottom Line: The reasoning behind such a criterion is that, if our purpose is to use gene regulatory networks to design therapeutic intervention strategies, then we are not concerned with network fidelity, per se, but only with our ability to design effective interventions based on the inferred network.The objective of a control policy is to optimally reduce the total steady-state probability mass of the undesirable states (phenotypes), which is equivalent to optimally increasing the total steady-state mass of the desirable states.Hence, when one is aiming at a specific application, it may be wise to use an objective-based measure of inference validity.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, Texas A & M University TX, USA.

ABSTRACT

There are two distinct issues regarding network validation: (1) Does an inferred network provide good predictions relative to experimental data? (2) Does a network inference algorithm applied within a certain network model framework yield networks that are accurate relative to some criterion of goodness? The first issue concerns scientific validation and the second concerns algorithm validation. In this paper we consider inferential validation relative to controllability; that is, if an inference procedure is applied to data generated from a gene regulatory network and an intervention procedure is designed on the inferred network, how well does it perform on the true network? The reasoning behind such a criterion is that, if our purpose is to use gene regulatory networks to design therapeutic intervention strategies, then we are not concerned with network fidelity, per se, but only with our ability to design effective interventions based on the inferred network. We will consider the problem from the perspectives of stationary control, which involves designing a control policy to be applied over time based on the current state of the network, with the decision procedure itself being time independent. The objective of a control policy is to optimally reduce the total steady-state probability mass of the undesirable states (phenotypes), which is equivalent to optimally increasing the total steady-state mass of the desirable states. Based on this criterion we compare several proposed network inference procedures. We will see that inference procedure ψ may perform poorer than inference procedure ξ relative to inferring the full network structure but perform better than ξ relative to controllability. Hence, when one is aiming at a specific application, it may be wise to use an objective-based measure of inference validity.

No MeSH data available.