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Simulation results presented in Studies 1 and 2 suggest that re-analysis of available genotype-phenotype data is likely to identify additional genetic variants when the multi-dimensionality of the phenotype, and the possibility of genetic effects being specific to certain phenotypic dimensions or items, are taken into account.
Thus, we can be less confident in reported variants when the sample size is limited.
This assumption may be reasonable for rare variants when the phenotype being studied reduces fitness.
This flow is helpful in understanding the role of genes or variants when the relationships between the genes and stroke or its etiologies are obscure.
We found that SeqEM is comparable to MAQ for common variants when the parameters are fixed close to their true values, but for rarer variants, SeqEM results in improved genotype-call error rates.
To infer the clinical meaning of particular gene variants, when the meaning is not already known, requires large population samples of genetic information that has been linked with clinical information about the tested individuals.
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The main goal of increasing diversity is to detect faults in variant when the variants fail on disjoint subsets of the input space.
The sugar factory FSOTDelectricity is a convenient variant when the goal is to increase electrification in poor regions without other alternative renewable energy supplies.
This is reassuring since we expect the two causal variants to behave as effectively a single variant when the LD is high.
Therefore, with even a moderate amount of coverage, most variants are covered by at least one read to another variant when the SD of the insert length is big enough.
We compared means with a t test, Welch variant when the Bartlett's test for equal variances was significant, and Kruskal-Wallis test when more than two groups were compared.
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