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Also, since the results for Test Set MCI also did not change significantly, it is reasonable to say that the sample pruning and usage of metafeatures introduced a good generalization in the signature.
SNP quality control and sample pruning was performed by means of Plink (version 1.9) [ 57].
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This is due to the fact that incomplete lineage sampling prunes tips from the tree, thus inflating the time interval between cladogenic events in the more recent past [9].
However, our phylogenies only included 91% (Cyt b) and 74% (Rag1) of the recognized species diversity in the focal clade and incomplete taxon sampling will bias our calculations of γ because incomplete lineage sampling prunes tips from the tree, thereby inflating the branch lengths in the recent past [ 54].
Analysis was started with a random tree sample (Subtree pruning and regrafting method) and 1000 bootstrap replicate runs.
DE-based estimates for the NTR/NESDA sample (when pruning the entire ~6 M SNP set) were 38%, 38%, and 34% for GGT, ALT, and AST, respectively.
The best and safest tool to use in cutting off the sample is pruning shears, which make a straight clean cut.
With 4x4 samples, DB pruning helps tTest to identify 18 more true DEGs, a 26.8% improvement.
Therefore, these analyses were instead based on a reduced dataset, in which the tree used for prior determination, as well as the ones in the 500-tree sample, were pruned to include only those taxa that had a known state for easy bleeding.
SERT activity data were not determined for eight species of Narcissus and these samples were pruned from the phylogenetic trees in the correlation tests.
Prior to evaluating the heterozygosity of the samples, we pruned the SNP dataset to avoid bias that could have been introduced by large quantities of SNPs in strong LD.
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