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Performance metrics of both validation and calibration datasets indicated that V 2 was a moderately good predictor of bole MOE.
Comparison between the CSHL and NIAS datasets indicated that 250 sites differed between the NIAS and CSHL individuals and were not allelic to each other (Additional file 10).
Their experimental results for the real datasets indicated that the proposed hybrid model can be an effective in improving forecasting accuracy achieved by either component.
Moreover, a simulation study using plasmode datasets indicated that the estimated fold-change in pairwise comparisons was the same using either linear mixed models or a comparative CT method, but the linear mixed model approach was more powerful.
The phylogenies inferred from the gag and pol datasets indicated that CRF28_BF and CRF29_BF are closely related and have co-evolved since their emergence.
Tests conducted on simulated as well as real datasets indicated that the new algorithm works well and outperforms methods based on global correlation measures, especially when there is substantial noise and moderate distortion in the data.
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Experimental results on two large real datasets indicate that the proposed incremental slope-one recommenders can correctly reflect the increments of dynamic datasets with high computational efficiency.
The test results of the ISPRS datasets indicate that the proposed algorithm achieved low commission errors ranging from 1.53% to 6.91%.
An extensive experimental comparison with two public EEG datasets indicates that the MKELM method gives higher classification accuracy than those of the other competing algorithms.
Extensive experiments on several commonly used image SR testing datasets indicate that the proposed method achieves state-of-the-art image SR results.
Experiments on two real-world datasets indicate that the quality of learned semantic vectors and the performance of social emotion classification can be improved by our models.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com