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We here define a 'discreteness' value given to each cluster.
As well another 488 (7.6%) clusters were recognized as differences between known gene structures and predicted transcripts as in the example of the cluster shown in Supplementary Figure S9 was also confirmed that the rate of overlaps of reference increases with the decrease of the discreteness value which was defined in Section 2.4 and given to each cluster (Supplementary Figure S10).
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The names that were eventually given to each of the clusters were determined post-hoc in an attempt to characterize the nature of each cluster in perceptual space.
The term undefined subtype was given to this cluster.
KMCA is an iterative algorithm and two steps are performed in every iteration: Step-1: Find the distances from a given observation to each cluster centre and label the observation with the label of the cluster centre which is the closest.
Enlarged copies of the maps showing each cluster were given to the interviewers with instructions about the cluster boundaries.
Since multi-stage cluster sampling technique was applied in which each cluster (block) was of different size, weighted analysis was performed by giving weights to each cluster (block) in proportion to its size [ 42, 43].
Randomization was stratified by cluster size and was designed to give each cluster an even probability of being randomized to intervention or control condition.
Here, it is assumed that the probability of a given pixel belonging to each cluster can be modeled by a particular probability distribution function.
The ability to give each cluster a P value is an important contribution.
Structure assumes Hardy-Weinberg equilibrium and uses Bayesian algorithms to infer the assignment probability of any given individual to each genetic cluster based on allele frequencies.
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