Bayesian Robustness in the Control of Gene Regulatory Networks
Bayesian Robustness in the Control of Gene Regulatory Networks
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DOI：
10.1109/tsp.2009.2022872
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发表时间：
2009-09-01
影响因子：
5.4
通讯作者：
Dougherty, Edward R.
中科院分区：
文献类型：
--
作者：
Pal, Ranadip;Datta, Aniruddha;Dougherty, Edward R.
The presence of noise and the availability of a limited number of samples prevent the transition probabilities of a gene regulatory network from being accurately estimated. Thus, it is important to study the effect of modeling errors on the final outcome of an intervention strategy and to design robust intervention strategies. Two major approaches applied to the design of robust policies in general are the min-max (worst case) approach and the Bayesian approach. The min-max control approach is at times conservative because it gives too much importance to the scenarios which hardly occur in practice. Consequently, in this paper, we focus on the Bayesian approach for the control of gene regulatory networks.The errors originating in the data extraction process, gene selection and network inference prevent the transition probabilities of a gene regulatory network from being accurately estimated. Thus, it is important to study the effect of modeling errors on the final outcome of an intervention strategy and to design robust intervention strategies. Two major approaches applied to the design of robust policies in general are the minimax (worst case) approach and the Bayesian approach. The minimax control approach is typically conservative because it gives too much importance to the scenarios which hardly occur in practice. Consequently, in this paper, we formulate the Bayesian approach for the control of gene regulatory networks. We characterize the errors emanating from the data extraction and inference processes and compare the performance of the minimax and Bayesian designs based on these uncertainties.
