These factors can be identified by analyzing the historical and contemporary data on patterns of resistance to drugs that have already failed and insight can be gained by modeling those patterns.
In addition, improved confidence in pathophysiological model parameter values may be gained by modeling simultaneously, rather than individually, the PK of several compounds that have been studied under the same clinical condition, be it a disease state, pregnancy, or comedication.
For looking at spatial correlations typical improvements can be gained by modelling smoothed or segmented log2 Cy3/Cy5) data.
No additional information was gained by modelling for TNFα, resistin, catecholamine infusion rate, sex, age, body mass index (BMI), operation time or medication.
No additional information was gained by modelling for TNFα, resistin, catecholamine infusion rate, gender, BMI, operation time, heart/lung bypass time or medication, suggesting a minor relevance of these parameters for inflammatory insulin resistance in our model.
Further improvements may be gained by introducing a model which can efficiently handle both the non-linear and the stochastic relations of the two modalities as well as the temporal dependency.
What, then, can be gained by having a realistic model over a simplified model?
These studies demonstrate the unique advances in understanding and prediction that can be gained by integration of computational modeling with quantitative experimental data on molecular and cellular networks.
Next, to assess how much additional performance could be gained by classifier tuning, SVM model complexity and kernel functions are optimized systematically from a selected set.
One purpose of this paper is to show the advantages that can be gained by utilizing a parametric model, which allows use of explicitly defined, continuous hazard and survival functions for tools in analysis.
Some improvement (AUC = 72.2) could be gained by removing from the model variables with inconsistent association with endometriosis across phase I and II data, but the external validity of the reduced model would need to be evaluated in an independent data set.
