Sentence examples for models using simulated from inspiring English sources

Davies et al. 2003 [32] built homology models of DRB1*0301, DRB1*0401, and DRB1*1101 using a DRB1*0101 template structure and docked peptides into these MHC models using simulated annealing optimization with the AMBER force field in explicit water.

We explore the behaviour of the models using simulated data sets, and illustrate their application to two real data sets of plant traits.

The objective of this paper was to develop three models that would permit multi-trait genomic selection by combining scarcely recorded traits with genetically correlated indicator traits, and to compare their performance to single-trait models, using simulated datasets.

We give theoretical arguments suggesting that the method is nearly optimal in performance, and demonstrate its effectiveness on broad classes of nonlinear genetic models using simulated human genomes and the small amount of currently available real data.

We compare these models using simulated data sets of allele-specific read counts under a null scenario in which there is bias (B≠0.5) and no allelic imbalance (R = 1) and simulated data sets with both bias and allelic imbalance (R≠1).

We evaluate the model using simulated and empirical backtests and analyze the impact of the insurance guarantees on the portfolio performance.

We evaluated the performance of this model using simulated metagenomes and demonstrate its applicability on three recent metagenomic datasets.

In Figure 2, we show the theoretical behavior of this model using simulated data with varying base qualities.

In order to determine whether the mean LAP scores varied by each of the simulated patient scenarios, Generalised Estimating Equations (GEEs) were used to fit a linear model using simulated patient scenario as the single independent variable (Table  1).

To provide insights into the decision rule criterion (in terms of BF threshold) to declare SNPs as subjected to selection, we subsequently investigated the power and the robustness of the model using simulated data (see Methods).

The clear-sky mL&J model used simulated data, representative of extraterrestrial insolation, while the real-sky mL&J model incorporated local meteorological data.