External Control in Markovian Genetic Regulatory Networks
Machine Learning
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Planning for gene regulatory network intervention
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Exploiting structure in policy construction
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 0.01 |
Controlling genes and interactions between them is an example real life problem that exhibits partial observability and can be modelled with POMDP framework. In this work, we explore the feasibility of realizing the genes related problem in POMDP framework. Current works addressing partial observability focus on formulating algorithms for the finite horizon gene regulatory network control problem. This motivated us to take the challenge and tackle the control problem from a real infinite horizon partially observable perspective. In other words, the method proposed in this work is a POMDP formulation for the infinite horizon version of the problem. This formulation first decomposes the problem by isolating different unrelated parts of the problem, and then makes use of existing POMDP solvers to solve the obtained sub problems, the final outcome is a control mechanism for the main problem.