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The problem is to choose c l and c u to minimize genotype misclassification.
By accurately estimating these parameters based on the likelihood of the sample data, we ensure consistent and asymptotically efficient estimates of genotype frequencies and the nucleotide-read error rate that should minimize genotype misclassification.
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This is a Bayes classification procedure that can be expected to minimize the overall genotype misclassification rate (i.e. genotype-call error).
Strict quality control for genotyping was used, minimizing the potential for genotype misclassification.
The effect of reducing the number of genotype parameters from two to one on genotype misclassification is discussed below.
To minimize exposure misclassification, we applied genotyping methods with low error rates (the Sequenom and Illumina methods have genotyping error rates of 0.5% and 0.3%, respectively), DNA samples were randomly assigned to the genotyping plates and the genotyping personnel were blinded to case control status.
Articles were inconsistent in reporting the data needed to assess selection bias and the methods used to minimize misclassification (of the genotype, outcome, and environmental exposure) or to identify population stratification.
Therefore, a moving average window with optimal window length and threshold was designed to minimize the misclassification.
Cohort studies designed to estimate human health effects of exposures to urban pollutants require accurate determination of ambient concentrations in order to minimize exposure misclassification errors.
The model was built in an iterative fashion with the number of gene features selected to minimize the misclassification error in cross validation.
3.3, parameters required for effective performance of GMDH are determined automatically so as to minimize the misclassification rate.
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CEO of Professional Science Editing for Scientists @ prosciediting.com