Exact(2)
Here we extract the same categories of significantly differentially expressed genes.
We extract the same amount of paired reads as the data size in L_RNA_scaffolder (8.8 million) for further scaffolding.
Similar(58)
To allow a comparison with FieldScreen, we extracted the same bounded ligands from the protein data bank [59, 60] and corrected the bonds lengths with MacroModel 9.6.
We extracted the same dependent measures here, and explored their modulation at the onset of the novel pairs.
We extracted the same number of components as there were variables (50).
If a trial had not adjusted for clustering, we extracted the same data as for the individually RCTs.
We extracted the same amount of the sequencing reads amplified by the same kit except the sequencing platform type to control the variables.
When the trial did not account for clustering in their analysis, we extracted the same data as for trials that randomize individuals.
To evaluate the bias in the comparison caused by the correctable sequencing errors from Hiseq and Miseq, we extracted the same amount of the sequencing reads amplified by the same kit but sequenced on Hiseq 2000 or Miseq, respectively.
In each run, we extracted the same number of random miRNA target pairs out of all predicted target pairs of the SZmiRNAs and identified TFBSs in the promoter of these random miRNA target genes, then calculated the number of FFLs.
-wrap> By using machine learning methods, we extracted the same molecular features that were strong disease correlates or had some statistical ability to distinguish classes: lung CXCL1, CXCL2, CXCL5, TNF, IFN-γ, IL-12; and two blood features – IL-2 and TNF.
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