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Similarly to the uniform datasets, in some instances the accuracy may decrease significantly (see, e.g., datasets 30×75).
However, it is worth noting that in some instances the accuracy may decrease significantly (see, e.g., datasets 30×50 and 30×75) and proportionally to the increment of the error ratio, by suggesting, as general trend, the fact that the higher the error ratio the more difficult is to recover the correct haplotype set.
However, in many instances, the accuracy estimates were slightly lower than those obtained with the full set of markers.
The common conclusion was that in most instances the accuracy of predicting genotypic values was comparable for the investigated approaches.
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For instance, the accuracy of this approach was 8.2% better with respect to the method proposed in [6] when compared on the same dataset.
For instance, the accuracy of predictions is based on material parameters, which are obtained mostly from complicated experimental studies or from the literatures.
For coverage 1×, for instance, the accuracy decreased from 85.44% to 66.23% for MTR and from 93.24% to 74.18% for LCA.
For instance, the accuracy of pose recapitulation for known cocrystal complexes leads us to believe that highly scoring covalent docking poses are in productive, on-path conformations, in contrast to the incompatible poses predicted by noncovalent docking (Supplementary Figure 2).
In both instances, when the accuracy and precision framed in admitted limits in such determinations added up to the quantification limit (± 20%).
For instance, the accuracies for the negative classes are comparable to those obtained with supervised machine learning classifiers, such as support vector machines or decision trees, which operate exclusively on the BioGRID SL/SS categories [27] [29].
For instance, the accuracies for BTA1, BTA16 and BTA28 from BEAGLE were 0.986, 0.984 and 0.981, respectively, when 20% SNPs were masked.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com