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Motivation: To increase the signal resolution for large-scale meta-analyses of genome-wide association studies, genotypes at unmeasured single nucleotide polymorphisms (SNPs) are commonly imputed using large multi-ethnic reference panels.
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Because fixed content SNP arrays became economically viable before either of the sequencing approaches, much has been written about the proportion of common sequence variants that can be imputed with commonly used, fixed content SNP arrays (e.g. [ 19]).
Multivariate imputation by chained equations (MICE) is commonly used for imputing missing data in epidemiologic research.
Various strategies are commonly employed to impute values for interval-measured data, including assignment of one-half the detection limit to nondetected values or of "fill-in" values randomly selected from an appropriate distribution.
Under nearest neighbour estimation, variables of interest (Y) are imputed for target elements, commonly pixels covering an area of interest, where Y has not been measured.
Missing values were imputed using multiple imputations.
This score is commonly applied as a quality filter for imputed genotypes and is equivalent to the RSQR_HAT value by MACH and the information content (INFO) measure by PLINK (44).
Irrespectively of the existing spectrum of techniques, the results of imputation are commonly numeric meaning that once the data have been imputed they are not distinguishable from the original data being initially available prior to imputation.
The remaining missing character states were imputed by replacing them with the most commonly observed character state for that SNP.
However, GBS commonly generates large amounts of missing data that must be imputed before fitting a genomic prediction model.
For respondents who chose not to answer the model question or circled more than one model, the health care practice model was imputed by using the model that was most commonly selected by the other respondents working in the same clinic.
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