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Benchmarking optimization methods for parameter estimation in large kinetic models
2018
Bioinformatics
Kinetic models contain unknown parameters that are estimated by optimizing the fit to experimental data. This task can be computationally challenging due to the presence of local optima and ill-conditioning. While a variety of optimization methods have been suggested to surmount these issues, it is difficult to choose the best one for a given problem a priori. A systematic comparison of parameter estimation methods for problems with tens to hundreds of optimization variables is currently
doi:10.1093/bioinformatics/bty736
pmid:30816929
pmcid:PMC6394396
fatcat:nkoagacfqzd2pk5ltab57kwcbi