研究目的
Investigating the use of regularization techniques to avoid overfitting in the parameter estimation of complex biochemical reaction networks due to the large number of uncertain parameters and limited, noisy data.
研究成果
Regularization techniques are essential for avoiding overfitting in the parameter estimation of biochemical reaction networks due to the large number of uncertain parameters and limited, noisy data. The study demonstrates that parameter set selection, Tikhonov regularization, and L1 regularization can effectively regularize the parameter estimation problem, with the choice of technique depending on the modeler’s preference and the specific requirements of the model. Cross-validation is emphasized as a critical tool for ensuring models generalize well to new data.
研究不足
The study acknowledges that the choice of regularization technique depends on the modeler’s preference and the desired qualities of the estimated parameters, indicating that no single method is universally best. The modest changes in mean-squared error (MSE) seen in the example reflect the good choice of initial nominal parameter values, suggesting more drastic results might be seen with less optimal initial values.
