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For each pair-wise comparison, we found a significant overlap between the datasets and the one described in this study (Table S2 in Additional file 1 and Additional file 3), further validating the results of our time-course experiment.
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The relationships among the families in clade 14 were uncertain, and differed between the analysis of the complete dataset and the one based on only nuclear loci.
There were strong, highly significant connectivity correlations (R = 0.75, 0.83, 0.83, and 0.80 respectively, each associated with a p-value of less than 2.2e-16 2.2e-16n the network betweenn all four datheets anetworkones reconstructed from any combasedion of the three dallsets.
The tanglegram between the network obtained by combining the strict consensus MP tree and the ML tree for the cpDNA dataset and the one obtained by the nuclear ITS dataset is shown in Figure 7.
The nine subsets are training datasets, and the remaining one subset is the test dataset.
For both the experimental datasets and the synthetic ones, we studied the sensitivity of our algorithm to structural variation.
FT-MIR spectra were collected (two replicates on each sample of the calibration datasets, and one replicate on each sample of the validation dataset) as % transmittance in the range of 5012 926 cm−1.
In these models, we defined one of the datasets as the "Reference" dataset and the other one as the "Testing" dataset.
In order to match clusters across trial sets, all possible combinations of cluster pairing in the two datasets were considered, and the one with the highest overlap proportion was used to calculate the similarity scores.
The predicate symbol tests whether one of the test data, is a member of the input dataset and the other one a member of the input data set.
Phylogenetic trees were built from two nucleotide alignments: the first one included the 77 vertebrate NAT sequences retrieved from genomic databases (referred to as the 'vertebrate dataset') and the second one was composed of 58 primate sequences, including both the 17 database sequences and the 41 newly generated ones (referred to as the 'primate dataset').
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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