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We took their responses and combined them with quantitative criteria to get our results.
A separate substitution model was assumed for each of the five data partitions, with substitution models selected by comparison of Bayesian information criterion scores (Table S 3).
As with the MrBayes analysis, a separate substitution model was assumed for each of the five data partitions, with substitution models selected by comparison of Bayesian information criterion scores (Table S3).
For the concatenated data sets, partitioned analyses were conducted, with data partitioned by gene, with the parameters of nucleotide substitution models unlinked across partitions.
Nonetheless, results suggest that quantitative criteria derived from sleep-log data may be useful for classification of primary insomnia.
Substitution model selection was conducted for each of the four data partitions by comparison of Bayesian information criterion scores.
We ran three variant analyses, one with all the data unpartitioned, a second with the data partitioned by codon position (a "codon" partitioning scheme, with rDNA genes considered as additional data partitions), and a third with the data partitioned by both gene and codon positions ("G×C," or gene by codon partitioning); see below for how the final partitioning schemes were set up.
This led us to expand our code partitioning approach with data partitioning and this work is reported in Section 4.
The proposed CDCA data partitioning method is evaluated in comparison with SOM, FC and GA based data partitioning methods.
Model selection for the mtDNA data partitions in the MCMC was carried out with MrModeltest2 v.2.3 [ 73] based on the 'Akaike information criterion' [ 74].
We first confirmed the microarray data with quantitative PCR.
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