Mathematical Programming: Series A and B
Parameter Estimation in Stochastic Logic Programs
Machine Learning
Machine Learning
Mathematical Modeling of the Influence of RKIP on the ERK Signaling Pathway
CMSB '03 Proceedings of the First International Workshop on Computational Methods in Systems Biology
Bayesian inference for differential equations
Theoretical Computer Science
Gaussian process regression bootstrapping
Bioinformatics
Modelling metabolic pathways using stochastic logic programs-based ensemble methods
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
Transactions on Computational Systems Biology VII
Multi-objective optimisation, sensitivity and robustness analysis in FBA modelling
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
Hi-index | 0.00 |
We propose a novel approach for parameter estimation in dynamic systems. The method is based on the use of bootstrapping for time series data. It estimates parameters within the least square framework. The data points that do not appear in the individual bootstrapped datasets are used to assess the goodness of fit and for adaptive selection of the optimal parameters. We evaluate the efficacy of the proposed method by applying it to estimate parameters of dynamic biochemical systems. Experimental results show that the approach performs accurate estimation in both noise-free and noisy environments, thus validating its effectiveness. It generally outperforms related approaches in the scenarios where data is characterized by noise.