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Displaying 1-6 out of 6 total
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...
 
Stochastic Model Simulation Using Kronecker Product Analysis and Zassenhaus Formula Approximation
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Mehmet Umut Caglar,Ranadip Pal
Issue Date:September 2013
pp. 1125-1136
Probabilistic Models are regularly applied in Genetic Regulatory Network modeling to capture the stochastic behavior observed in the generation of biological entities such as mRNA or proteins. Several approaches including Stochastic Master Equations and Pr...
 
Steady-state preserving reduction for genetic regulatory network models
Found in: Computer-Based Medical Systems, IEEE Symposium on
By Ranadip Pal, Sonal Bhattacharya
Issue Date:August 2009
pp. 1-6
Fine-scale models based on stochastic differential equations can provide the most detailed description of the dynamics of gene expression and imbed, in principle, all the information about the biochemical reactions involved in gene interactions. However, t...
 
An integrated approach to anti-cancer drug sensitivity prediction
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics
By Noah Berlow,Saad Haider,Qian Wan,Mathew Geltzeiler,Lara Davis,Charles Keller,Ranadip Pal
Issue Date:May 2014
pp. 1
A framework for design of personalized cancer therapy requires the ability to predict the sensitivity of a tumor to anticancer drugs. The predictive modeling of tumor sensitivity to anti-cancer drugs has primarily focused on generating functions that map g...
 
Stochastic Model Simulation Using Kronecker Product Analysis and Zassenhaus Formula Approximation
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Mehmet Umut Caglar, Ranadip Pal
Issue Date:September 2013
pp. 1125-1136
Probabilistic Models are regularly applied in Genetic Regulatory Network modeling to capture the stochastic behavior observed in the generation of biological entities such as mRNA or proteins. Several approaches including Stochastic Master Equations and Pr...
     
Transient Dynamics of Reduced-Order Models of Genetic Regulatory Networks
Found in: IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
By Ranadip Pal, Sonal Bhattacharya
Issue Date:July 2012
pp. 1230-1244
In systems biology, a number of detailed genetic regulatory networks models have been proposed that are capable of modeling the fine-scale dynamics of gene expression. However, limitations on the type and sampling frequency of experimental data often preve...
     
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