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To verify the superiority of Method A, we quantified the template amount using the MultiNA Microchip Electrophoresis System DNA-500 Kit.
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Statistical analysis indicated the superiority of methods over other available models.
Retrospective validation studies demonstrate the superiority of methods which combine both shape and pharmacophore information on the family of peroxisome proliferator-activated receptors (PPARs).
Simulation studies have suggested the superiority of methods based on some sort of variable selection over GBLUP [ 2, 38- 40].
For traits involving a limited number of large-effect QTL, simulation studies have consistently predicted superiority of methods using variable selection and differential shrinkage of estimates of effects such as BayesB.
This also represents a confirmation of results from simulations that anticipated superiority of methods performing variable selection and differential shrinkage of estimates, especially with traits having some large-effects QTL (Meuwissen et al. 2001; Daetwyler et al. 2010b).
Superiority of MLSDS method over other methods was shown.
The results show the superiority of our method compared to other methods.
Furthermore, the comparison of recognition results with other three feature extraction methods demonstrates the superiority of this method.
Systematic experimental results verify the superiority of our method over existing active learning methods.
Next we analyzed diffuse large B cell lymphoma (DLBCL) data sets to show the superiority of our method to standard supervised prediction methods (Example 2).
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