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MissForest is a highly accurate method of imputation for missing laboratory data and outperforms other common imputation techniques in terms of imputation error and maintenance of predictive ability with imputed values in two clinical predicative models.
We focused mainly on the rare variants, as these are more difficult to impute and most can be gained in terms of imputation quality when using a better reference set.
The experimental results indicate that GL2P outperforms its competitors in terms of imputation accuracy and better preserves the structure of differentially expressed genes.
RFI was the most promising method overall because of its consistently high performance in terms of imputation accuracy and subsequent GS accuracy; however, it was the most computationally intensive method evaluated.
Nevertheless, for all densities, both the haploid and diploid ADCL panels consistently outperformed their PD counterparts in terms of imputation accuracy across all sites, as well as across only the low-frequency variants.
In terms of imputation to missing values, no assumption of minimum requested items answered has been made due to the low number of missing responses in the validation study.
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Results were quantified in terms of imputation-adjusted ORs based on Rubin's rule along with 95% confidence intervals (CIs).
Using four empirical datasets, we evaluate and characterize four such imputation methods, referred to as k-nearest neighbors, singular value decomposition, random forest regression, and expectation maximization imputation, in terms of their imputation accuracies and the factors affecting accuracy.
By using array-based genotypic datasets with varying levels of simulated missing data, we compared these methods in terms of their imputation accuracy, computation time, and impact on GS prediction accuracy.
However, in terms of correlation coefficient, imputation accuracy was slightly greater for crossbred animals than for Yorkshire pigs, but slightly lower for crossbred animals than for Landrace pigs.
Although it was physically unfeasible for us to collect all the original data without imputation, in the present meta-analysis we considered the potential impact on the review result through a sensitivity analysis in terms of the influence of imputation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com