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The result shows that the proposed method can robustly and effectively solve the global stitching problem for very complex STL models.
On simulated data, the method can robustly identify alien genes irrespective of the coding nucleic acid found in a virus.
According to the results, this method can robustly recognize the difference between healthy people and patients with heart diseases.
Results in Table 1 show that the method can robustly identify the correct regulatory model even if the parameters are varied by 100 fold.
In summary, these results suggest that our method can robustly capture important MFRMs relevant to diseases when applied to RNA-Seq data.
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It is not known whether modern imputation methods can robustly address the problem of missing data, and whether predictive models will remain accurate when imputed values are used.
It is not known whether imputation methods can robustly address the problem of missing data, and whether predictive models will remain accurate when imputed values are used.
The presented method can be robustly deployed in the ecological monitoring of mountain forests.
Through the analysis above, phase error estimation method can perform robustly with the existence of position errors.
Simulations show that the fit method can indeed robustly discriminate between the effects of different model parameters on the shape of the sucrose response, that is, changes in one model parameter are reliably detected with the other model parameters being invariant.
Both false-positives and false negatives from the above example sources of error can theoretically be addressed through sufficiently high genomic coverage, coupled with methods that can robustly leverage the additional data.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com