Discrete optimized protein energy (DOPE) suggested that the first model generated by Modeller (M1) revealed the lowest energy (−40008.88).
The performance of the model generated by constraint-based modeling method is significantly improved compared to the model obtained by only fitting the quantitative data.
We compared the performance of the model generated by constraint-based modeling method and that of the model obtained by only fitting the time course data.
At the end, the statistical significance of the enrichment scores are estimated by comparison to a null model generated by permuting the samples uniformly at random.
At the end, significance values (P-value and normalized enrichment score) were computed by comparing each regulon enrichment score to a null model generated by randomly and uniformly permuting the samples 1000 times.
An important aspect of the model generated by Reddy and colleagues is that it provides a powerful mechanism for sensory normalization without the need for any recurrent neural processing.
This paper presents the simplified model generated by response surface methodology (RS M for analyzing basic performance characteristics of the Maisotsenko cycle heat and mass exchanger.
Diffusion rate and Zn doping concentration in diffused layer are characterized by a response model generated by training feed-forward error back-propagation neural networks.
The quadratic model generated by RSM is used to predict the performance parameters, i.e. Nusselt number and friction factor with reasonably good accuracy.
We study a random design regression model generated by dependent observations, when the regression function itself (or its ν-th derivative) may have a change or discontinuity point.
Using a model generated by the D-optimal design, a series of three-dimensional response spaces for SOD liposome encapsulation efficiency were established.
