Sentence examples for based on evaluations using from inspiring English sources

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Our results also demonstrate that conclusions regarding the superiority of one prediction method over another can be misleading when they are based on evaluations using benchmark datasets with a high degree of sequence similarity (e.g., the benchmark dataset of unique peptide sequences).

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Moreover, based on our evaluations using MiBench benchmark suites, the reusable structures constitute to only 2% of all the custom instruction instances.

Based on tractographic evaluations using magnetic resonance imaging/diffusion tensor imaging of the medulla oblongata in the brain stem, we found that the connectivity between the NTS and NA was disrupted in neurogenic hypertension, concurrent with impairment of the cardiac vagal baroreflex as detected by radiotelemetry.

The main analyses were performed using an intention-to-treat (ITT) approach to structuring of data, based on that evaluations using the ITT and completer approaches in parallel have been called for when reporting from routine clinical psychological interventions.

We defined impact as EMR adoption and effect on physician practice, based on evaluation measures used in the studies.

Much has been written about the effectiveness of active labour market measures based on evaluations carried out using micro-data, yet not enough about their effectiveness at the aggregate level.

In the second stage, each of the 15 generations were simulated by mating 20 males and 500 females that were each selected from a set of 1000 selection candidates of one sex based on BLUP evaluation using pedigree and phenotype information from the current and all previous generations.

However, the development of new car seats is very time consuming and costly since it is typically based on experimental evaluation using prototypes.

The ANN model was found to be superior to RSM model for its mapping abilities based on statistical evaluation using the coefficient of determination (R2), root mean square error (RMSE) and standard error of prediction (SEP).

Based on the evaluation using the system of criteria mentioned above and the comparison of numerical results for the suite of test datasets, the software tools gmkin, KinGUII and CAKE are recommended for use type I, and gmkin and KinGUII for use type II.

Based on their evaluation using an in vivo ChIP-chip dataset, their method outperforms four previous methods [ 1, 11- 13] by reporting fewer false positive matches.

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