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Which Control Gene Should be Used in Genetic Regulatory Networks?
Found in: Statistical Signal Processing, IEEE/SP Workshop on
By Golnaz Vahedi, Aniruddha Datta, Edward R. Dougherty
Issue Date:August 2007
pp. 6-10
Probabilistic Boolean Networks (PBNs) are rule-based models for gene regulatory networks. Previously, we proposed a method for finding the control policies with the highest effect on steady-state distributions of PBNs. To this end, the theory of infinite-h...
 
Bayesian Robustness in the Control of Gene Regulatory Networks
Found in: Statistical Signal Processing, IEEE/SP Workshop on
By Ranadip Pal, Aniruddha Datta, Edward R. Dougherty
Issue Date:August 2007
pp. 31-35
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 o...
 
On Reinforcement Learning in Genetic Regulatory Networks
Found in: Statistical Signal Processing, IEEE/SP Workshop on
By Babak Faryabi, Aniruddha Datta, Edward R. Dougherty
Issue Date:August 2007
pp. 11-15
The control of probabilistic Boolean networks as a model of genetic regulatory networks is formulated as an optimal stochastic control problem and has been solved using dynamic programming; however, the proposed methods fail when the number of genes in the...
 
Fuzzy Intervention in Biological Phenomena
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Aniruddha Datta, Edward R. Dougherty, Hazem N. Nounou, Mohamed N. Nounou, Nader Meskin
Issue Date:November 2012
pp. 1819-1825
An important objective of modeling biological phenomena is to develop therapeutic intervention strategies to move an undesirable state of a diseased network toward a more desirable one. Such transitions can be achieved by the use of drugs to act on some ge...
     
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