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Table 1 SPRINT algorithm flow Function Partition(DataSet S {S training set} Begin If (all s∈S the same mark) then Return; Foreach a∈A Do calculate column attribute a split S into S1 and S2 using the best split attribute Partition(S1); Partition(S2); End SPRINT Fig. 1 Example of attribution list.
Thus, when appropriate, a multiple partition dataset was used for HIV-1 Control analysis.
A multiple partition dataset was used for the identification of codons under selection in HIV-1 Control analysis.
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The partitioning strategies that explained the data set with the least random error were the maximum partition datasets in both analyses [ 61].
For the gene-codon-partition dataset, we used the GTRMIX option of RAxML.
The traditional morphology-based groupings, Acari and Acaromorpha (Acari + Ricinulei), were statistically rejected by the AU test employing the three-partition dataset (Table 1).
comm).. Whether further partitioning of the data would yield an improvement over the 4-partition dataset was investigated by calculating the Bayes Factor (B10), which is robust against Type I error ([ 27], J. Brown, pers. comm).
A dataset with 26 partitions (12S rRNA, 16S rRNA, ND1, ND2, CO1, CO2+ATP8+ATP6, CO3+ND3+ND4L, ND4, ND5, CYTB, with all protein-coding genes being split into three partitions comprising 1st, 2nd and 3rd codon positions) represented H1 and the 4-partition dataset represented H0.
Bayes factor results showed that the most highly partitioned dataset is the preferred strategy among different partitioning schemes.
k-means partitions datasets into subsets based on their fitness value.
In partitioned datasets a different substitution model was applied to the introns, the first, second, and third codon positions.
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