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The markers that showed LOD scores from IM higher than the chromosome wide threshold with P < 0.05 from the 1,000 permutations were selected as the cofactor.
To quantify the explained variance, QTLs above the 5%% significance threshold (using 1000 permutations) were selected and fitted into a multiple QTL model using the function fitqtl based on the Haley Knott regression method.
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When that block is full, another permutation is selected for the next group of patients.
For each block, a permutation is selected at random and patients are assigned to treatment as they are enrolled according to that permutation.
In other words, a random permutation of the nodes is generated, the state of each node is updated using the most recent states of its regulators, then a different permutation is selected randomly, and so on.
Reads were sequenced randomly from these transcripts according to a power-law distribution modified by read length: in generating read sequences, each transcript t i in position i of the permutation was selected with probability proportional to P[ t i ]∝ l(t i )/ i where l is the length function, measured in bases.
The trend lines that provided the best fit to observed data, based on Monte Carlo permutation tests, were selected.
Then, in each permutation, n genes were selected from all human genes whose length is ± 50 kb of the average length of the n ranked genes.
Then the profiles with statistically significant higher number of genes were selected using permutation test.
Thus, only the 203 probe sets with ≥35 permutations passing the 1.3 fold cutoff were selected (Fig. 2, red dots).
For the SAM analysis, the differentially expressed genes were selected, following 100 permutations, at a maximum predicted false discovery rate of 5% and the same minimum fold-change of ± 1.5.
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