TY - GEN
T1 - Comparison of robust strategies for the control of gene regulatory networks
AU - Pal, Ranadip
AU - Datta, Aniruddha
AU - Dougherty, Edward
N1 - Copyright:
Copyright 2008 Elsevier B.V., All rights reserved.
PY - 2008
Y1 - 2008
N2 - 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 Mini-Max (worst case) approach and the Bayesian approach. In this paper we will compare the Minimax, Bayesian and Global robustness approach with respect to intervention in genetic regulatory networks.
AB - 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 Mini-Max (worst case) approach and the Bayesian approach. In this paper we will compare the Minimax, Bayesian and Global robustness approach with respect to intervention in genetic regulatory networks.
UR - http://www.scopus.com/inward/record.url?scp=51549094241&partnerID=8YFLogxK
U2 - 10.1109/GENSIPS.2008.4555671
DO - 10.1109/GENSIPS.2008.4555671
M3 - Conference contribution
AN - SCOPUS:51549094241
SN - 9781424423729
T3 - GENSIPS'08 - 6th IEEE International Workshop on Genomic Signal Processing and Statistics
BT - GENSIPS'08 - 6th IEEE International Workshop on Genomic Signal Processing and Statistics
Y2 - 8 June 2008 through 10 June 2008
ER -