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This validation generated the results shown in Table 1, providing an overview on the conservation of individual subunits throughout representative eukaryotic lineages.
The performance of the resulting DETECT algorithm is presented in figure 2, and a nomogram of the DETECT algorithm for use in clinical practice is shown in figure 3. Internal bootstrap validation generated consistent results and confirmed overall performance of the algorithm (see online supplementary text and tables S8 and S9).
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4 Validation generates confidence in the accuracy and reliability of a CFD model.
The l 1-l2 regularization and the cross validation generates an unbiased and objective output with a low classification error.
However, when the homogeneous subgroups were used, both the 7-fold and double cross-validation generated comparable Q values and remained high (>0.78, permutation test of p < 10 5 for all data sets).
To make a further validation, we generated all 746 drug-specific validation sets and 817 ADR-specific validation sets for drugs and ADRs, respectively.
For validation we generated smoothly-time varying channels using the RCM and used the three validation metrics described in the previous paragraphs.
The cases for training and validation were generated by the real LTE network.
Extensive validation on generated dataset and real data show the effectiveness of the algorithm.
The validation results generated from such different approaches proved once again that equally robust solutions of any complex physical process can be reached from numerous models.
From each depth image, we select an average 200 of superpixels, and in total, approximately 120 thousand input windows for training and 20 thousand for validation are generated.
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