A model for reasoning about persistence and causation
Computational Intelligence
Algebraic decision diagrams and their applications
ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
Stochastic dynamic programming with factored representations
Artificial Intelligence
Coefficient of determination in nonlinear signal processing
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
On Learning Gene Regulatory Networks Under the Boolean Network Model
Machine Learning
External Control in Markovian Genetic Regulatory Networks
Machine Learning
Dynamic Programming
Optimal and Approximate Stochastic Planning using Decision Diagrams
Optimal and Approximate Stochastic Planning using Decision Diagrams
Markov Decision Processes Based Optimal Control Policies for Probabilistic Boolean Networks
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Efficient solution algorithms for factored MDPs
Journal of Artificial Intelligence Research
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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
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Controlling gene regulatory networks (GRNs) is an important and hard problem. As it is the case in all control problems, the curse of dimensionality is the main issue in real applications. It is possible that hundreds of genes may regulate one biological activity in an organism; this implies a huge state space, even in the case of Boolean models. This is also evident in the literature that shows that only models of small portions of the genome could be used in control applications. In this paper, we empower our framework for controlling GRNs by eliminating the need for expert knowledge to specify some crucial threshold that is necessary for producing effective results. Our framework is characterized by applying the factored Markov decision problem (FMDP) method to the control problem of GRNs. The FMDP is a suitable framework for large state spaces as it represents the probability distribution of state transitions using compact models so that more space and time efficient algorithms could be devised for solving control problems. We successfully mapped the GRN control problem to an FMDP and propose a model reduction algorithm that helps find approximate solutions for large networks by using existing FMDP solvers. The test results reported in this paper demonstrate the efficiency and effectiveness of the proposed approach.