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The Optimal Experiment Design (OED) methodology for parameter estimation (based on the Fisher information matrix) relies on a set of nominal parameters.
The focus of this paper is on the presentation of a methodology for parameter identifiability, which can be systematically applied for qualitative experimental design.
To select features from the generated gene-alteration profiles that distinguish cancer drivers from NGs, we followed the methodology for parameter tuning from the TUSON (TUmor Suppressor and ONcogene) explorer [ 19] (Materials and Methods).
Following the methodology for parameter tuning as described in the TUSON explorer [ 19], we applied the Lasso approach [ 46] to identify the most reliable features for predicting OGs and TSGs.
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This paper presents a methodology for parameters optimization applied to an hybrid behavioral control architecture.
This review article discusses existing modeling frameworks and methodologies for parameter estimation and model validation in the field of anaerobic digestion processes.
New methodologies for parameter estimation in complex models, new strategies for covariate and random effects modeling, and new diagnostics for time-to-event data and random effects components have already been developed and are among the 16 DDMoRe scientific contributions to the 2012 PAGE conference.
Second, the model offers an excellent parameter optimization methodology for fast parameter (internal and/or external) selection and performance evaluation.
The methodology used for parameter setting can be applied to other metaheuristics for solving complex problems in practice.
This paper proposes a genetic algorithm-based methodology for network parameter estimation and correction.
In this work, a methodology for the parameter estimation of heterogeneously-catalyzed reactions is presented.
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