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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.


Related in: MedlinePlus

Performance comparison of five network inference algorithms by different validity indices based on simulated BNps with 7 genes and K = 3. (A) Average normalized Hamming distance μham; (B) μss; (C) average undesirable steady-state mass π after applying derived stationary control policies based on inferred networks to the original ground truth BNps, compared to the average undesirable mass obtained by the optimal control policy (OPT) based on the complete knowledge of original BNps and the average undesirable mass before intervention (Original).
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Figure 2: Performance comparison of five network inference algorithms by different validity indices based on simulated BNps with 7 genes and K = 3. (A) Average normalized Hamming distance μham; (B) μss; (C) average undesirable steady-state mass π after applying derived stationary control policies based on inferred networks to the original ground truth BNps, compared to the average undesirable mass obtained by the optimal control policy (OPT) based on the complete knowledge of original BNps and the average undesirable mass before intervention (Original).

Mentions: Figure 2 plots μham, μss, and the average undesirable steady-state mass using the control policy designed on the inferred network via the MSSA algorithm. For comparison purposes, the latter average is compared to the average original undesirable mass and the average undesirable mass following application of the MMSA control policy designed on the original network. As m increases from 10 to 60, all algorithms improve. In fact, with more than 50 observed state transitions for these generated random BNps, the derived stationary control policies achieve almost the same performance compared to the optimal control policies with complete knowledge of the network models. The average performances from inferred networks are in fact within 5% for all five inference algorithms when M = 60.


Validation of gene regulatory network inference based on controllability.

Qian X, Dougherty ER - Front Genet (2013)

Performance comparison of five network inference algorithms by different validity indices based on simulated BNps with 7 genes and K = 3. (A) Average normalized Hamming distance μham; (B) μss; (C) average undesirable steady-state mass π after applying derived stationary control policies based on inferred networks to the original ground truth BNps, compared to the average undesirable mass obtained by the optimal control policy (OPT) based on the complete knowledge of original BNps and the average undesirable mass before intervention (Original).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Performance comparison of five network inference algorithms by different validity indices based on simulated BNps with 7 genes and K = 3. (A) Average normalized Hamming distance μham; (B) μss; (C) average undesirable steady-state mass π after applying derived stationary control policies based on inferred networks to the original ground truth BNps, compared to the average undesirable mass obtained by the optimal control policy (OPT) based on the complete knowledge of original BNps and the average undesirable mass before intervention (Original).
Mentions: Figure 2 plots μham, μss, and the average undesirable steady-state mass using the control policy designed on the inferred network via the MSSA algorithm. For comparison purposes, the latter average is compared to the average original undesirable mass and the average undesirable mass following application of the MMSA control policy designed on the original network. As m increases from 10 to 60, all algorithms improve. In fact, with more than 50 observed state transitions for these generated random BNps, the derived stationary control policies achieve almost the same performance compared to the optimal control policies with complete knowledge of the network models. The average performances from inferred networks are in fact within 5% for all five inference algorithms when M = 60.

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.


Related in: MedlinePlus