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A recent comparison of different orthology detection strategies has shown that phylogenetically based methods perform better than classical similarity search based methods [ 25].
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The results reveal that the proposed rule-based ST and AdaBoost based method performs better than the other methods viz., SVM and Decision Tree (DT), under varied noise conditions as well as under varied amount of data used for training.
At the bottom are SCCooccurrence and SCWiki, which implies that graph-based methods perform better than clustering-based methods on the dataset.
The performances of these methods were evaluated on blind dataset where machine learning-based methods perform better than QM-based method.
The experimental results have shown that all ANS-based methods perform better than ETSI-XAFE at this SNR, indicating that the methodology used in autocorrelation-based approaches is performing very well in very low SNRs, in comparison to that of ETSI-XAFE.
We have shown that our shrinkage regression-based methods perform better than the existing regression-based methods.
The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate.
However, in many cases the performance of all tested methods degraded after this removal, indicating that signature-based methods perform better at lower taxonomic distances.
As expected, when aligning pairs of proteins with weak sequence homology, a structure based method performs much better.
Overall, the hybrid-property based method performs a little better than the motif-based method.
The a priori based level set methods perform better than edge-based level set method; especially our method gets highest accuracy and makes less false segmentation.
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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