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Molecules belonging to the whole dataset were subjected to CODESSA and E-Dragon calculations in order to compute a large number of molecular descriptors enabling the construction of classification models.
All ORFs in the dataset were subjected to orthologous protein grouping using OrthoMCL v.2.0.1 [ 69, 70].
The sequences of 63 isolates from our dataset were subjected to the Glimmer gene prediction software [ 16] to provide gene predictions of equal and high quality.
A total of 188 full-length CDV hemagglutinin (H) gene sequences dataset were subjected to recombination analysis, including seven from modified live vaccine (MLV) strains and 12 from Taiwan specimens.
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The 'ranked-and-fused' dataset is subjected to conversion by linear and three-layer perceptrons.
To obtain unique tags and to determine the number of tags in the dataset, the dataset was subjected to redundancy treatment using Mothur software (v. 1.27.0) (Schloss et al. 2009).
Each simulated dataset was subjected to a maximum likelihood analysis as described above, with and without monophyly constraints.
Each dataset is subjected to the following preprocessing steps to find out the genes with most variability across the samples.
The ITS rDNA sequence dataset was subjected to phylogenetic analysis as well as clustering optimization using OPTSIL software.
The resulting dataset was subjected to statistical analysis [20], [21] and at a ProteinProphet probability score of 0.5 or higher (corresponding to a false identification rate of 3.5%), 612 proteins were identified and quantified.
In order to identify a cohort of genes differentially expressed between our defined groups of melanomas, the gene expression array dataset was subjected to the microarray data analysis program Significance Analysis of Microarray (SAM) [19].
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