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Firstly, we randomly select 10 samples from size 3288 for each dataset (set A or E).
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LOD threshold for each dataset was set based on a permutation test (1,000 permutation, P = 0.05).
The dataset set for that survey was obtained from BPS-Statistics Indonesia.
The non-cartilage arrays were a subset of the training dataset set aside for this validation only.
The influence of the different parameters was assessed for each dataset by setting (omega _{{mathrm{Frag}}}, omega _{{mathrm{RT}}}) and (omega _{{mathrm{Refs}}}) to either 0 or 1 again; these results are presented in Table 4.
For each dataset, a set of plausible topologies was selected from the study of the phylogenetic signal of the markers.
For each dataset, invariant set normalization was performed using the PM/MM model for calculating signal intensities in dChip 2006 [ 51].
For each dataset, two sets of cleaned variables were computed – a minimally cleaned set and a heavily cleaned set.
The results were collected for the 1000×100 generated dataset setting.
For each dataset and gene set, we applied k-means clustering with k = 2, 3, 4, 5, and 6 to divide each sample into two, three, four, five, or six groups based on the gene expression pattern of the gene set and applied the log-rank test to infer the statistical significance of differences in survival between the groups.
For each dataset, a complete set of 105 compounds were included.
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CEO of Professional Science Editing for Scientists @ prosciediting.com