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In the above mentioned assessment (Almagro et al., 2011), none of the tested methods was able to provide sufficiently good predictions for H3; the average RMSD for the best methods was ∼3 Å.
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The best methods are usually lifestyle changes.
The best methods were then validated in the human set-up.
The performance of each method is quantified and compared based on three norms and the best methods are identified.
Next best methods are supervised learners KNN and Gaussian NB.
The best methods are those which involve pressing the layered sample in KBr pellets.
These best methods are used to acquire the final results in the fourth step.
The 3rd best methods are ICA and SVM.
Results of the comparison of the two best methods are shown in Figure 3.
The second best methods were FFP and kmacs with k = 3, 4 and 64 ≤ k ≤ 117.
For the other two datasets, GSE36700 and E-GEOD-29746, the best methods were IBk and SVM.
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