Sentence examples for both simulation datasets and from inspiring English sources

The proposed method is applied to both simulation datasets and real experimental datasets.

In this section, we apply our method to both simulation datasets and yeast eQTL datasets (Brem and Kruglyak, 2005) to demonstrate the performance of our method.

We have applied BHIT to both simulation datasets and experimental soybean oil/protein content datasets, and we were able to obtain high accuracy and reliable results on both datasets.

To study the effectiveness of the proposed method, the Huber group LASSO is applied to inferring GRNs from both simulation datasets and real experimental datasets and the results of Huber group LASSO are compared with those from group LASSO in both area under receiver operating characteristic (AUROC) curve and area under the precision and recall (AUPR) curve.

The applications of the proposed method to the simulation datasets and real experimental datasets show that Huber group LASSO outperforms the group LASSO in both AUROC and AUPR.

The proposed model is evaluated on two different datasets, including simulation dataset and real dataset, and state-of-the-art performance is achieved compared with other methods.

At last, on the basis of the above two experiments, the classification and convergence performance of the GVCA method were tested on simulation dataset and UCI dataset, respectively.

To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets.

Of these, 400 were selected from the heterozygous simulation dataset, and 100 from the homozygous simulation dataset (the 4 1 ratio was based on experimentally validated genotypes from our earlier study, Stewart et al. 2011).

The results of our experiments show that ARLS can efficiently analyze a large amount of unstructured simulation datasets by distributing datasets and computations over the cluster of commodity machines.

The physical model is used to generate simulation datasets for developing and evaluating the diagnostic scheme that will be further calibrated with real world test data during application.