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The full datasets for the three evolutionary runs used in this study are given in Table S10.
However, this does not appear in the datasets for the three diseases under this study and would rarely appear in other real applications.
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We generate four synthetic massive datasets for the four algorithms using two real-world data sets: ClueWeb Category B [38] and BigCross [39].
Significant improvement in accuracy is achieved for some datasets for the two families of induction algorithms used: decision trees and Naive-Bayes.
To compile quantitative measurement of the identified peptides, the datasets for the two samples were processed for finding features (unique mass classes) based on the algorithm described previously [41].
Table 2 contains the mean and standard error for each of the six datasets for the four methods.
The numbers of animals in the reference and test datasets for the two validation procedures are shown in Table 1.
The predictive performance of enDNA-Prot trained with different training datasets for the two independent datasets is given in Table 5.
Our protein and PPI datasets for the four species are available at (under Tools and Data/Databases and Datasets), from the link "PPI – Shin et al. (2009)".
We calculated Spearman rank-correlations for each of the 100-taxon simulated datasets for the six supertree methods that consistently performed reasonably well (MinFlip, MRP, Q-Imp, QMC(All), QMC(Exp+TSQ), and RFS).
The benefit of the HLI step was evaluated by phasing the Ne100 60 k datasets for the eight pedigrees of a single replicate of simulated data using the full LRPHLI algorithm and the full algorithm with the HLI step turned off.
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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