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In the experiments, we observe that FSG performs better than the popular distance learning and ensemble learning algorithms when the shareability measure is large enough such that most of the samples are correctly classified by at least one of the base-layer classifiers.
It is critical that the samples are correctly labelled as far as possible.
When assuming two populations (K = 2), all 162 samples are correctly assigned to their species.
These samples are always assigned to the model set using the Kennard and Stone method and then the test samples are correctly predicted.
By this algorithm, 96% of DCIS and 95% of IG-IDC and ILC samples are correctly classified with an overall accuracy of 96%.
The flexibility of the diagnostic tests ensures that samples are correctly genotyped even in the presence of additional sequence variations that flank the polymorphisms of interest.
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All samples were correctly identified to the species level.
All samples were correctly identified, although there was some DNA degradation after processing.
All the 45 HLA-B*5701 positive and the 55 HLA-B*5701 HLA-B*5701amples were correctly idenegative
For the avian influenza samples, 3 out of 4 H5N1 samples and 2 out of 2 H7N9 avian samples were correctly identified.
Altogether, 94.6% of the NAT samples were correctly correlated with the corresponding healthy tissue (Fig. 1b), supporting general comparability between the datasets.
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CEO of Professional Science Editing for Scientists @ prosciediting.com