Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Journal of Mathematical Imaging and Vision
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Bayesian robust optimal linear filters
Signal Processing
External Control in Markovian Genetic Regulatory Networks
Machine Learning
Mathematics of Operations Research
Robust markov decision processes with uncertain transition matrices
Robust markov decision processes with uncertain transition matrices
Pattern Recognition
Optimal infinite-horizon control for probabilistic Boolean networks
IEEE Transactions on Signal Processing - Part II
Robust Intervention in Probabilistic Boolean Networks
IEEE Transactions on Signal Processing
Stationary and structural control in gene regulatory networks: basic concepts
International Journal of Systems Science - Dynamics Analysis of Gene Regulatory Networks
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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.