Results 11 to 20 of about 375,599 (291)
Animal, 2014 In livestock, many studies have reported the results of imputation to 50k single nucleotide polymorphism (SNP) genotypes for animals that are genotyped with low-density SNP panels.
Aniek C Bouwman +2 more
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Annual Review of Genomics and Human Genetics, 2009 Genotype imputation is now an essential tool in the analysis of genome-wide association scans. This technique allows geneticists to accurately evaluate the evidence for association at genetic markers that are not directly genotyped. Genotype imputation is particularly useful for combining results across studies that rely on different genotyping ...
Yun Li +3 more
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Agriculture, 2022 Imputation can be used to obtain a large number of high-density genotypes at the cost of procuring low-density panels. Accurate imputation requires a well-formed reference population of high-density genotypes to enable statistical inference. Five methods
Rudi A. McEwin +6 more
doaj +1 more source
Molgenis-impute: imputation pipeline in a box [PDF]
BMC Research Notes, 2015 Genotype imputation is an important procedure in current genomic analysis such as genome-wide association studies, meta-analyses and fine mapping. Although high quality tools are available that perform the steps of this process, considerable effort and expertise is required to set up and run a best practice imputation pipeline, particularly for larger ...
Kanterakis, Alexandros +5 more
openaire +2 more sources
Methods to Handle Incomplete Data
MAMC Journal of Medical Sciences, 2020 Context: The major question for data analysis is determining the appropriate analytic approach in the presence of incomplete observations. The most common solution to handle missing data in a data set is imputation, where missing values are estimated and
Vinny Johny +2 more
doaj +1 more source
The Annals of Applied Statistics, 2023 Given the prevalence of missing data in modern statistical research, a broad range of methods is available for any given imputation task. How does one choose the `best' imputation method in a given application? The standard approach is to select some observations, set their status to missing, and compare prediction accuracy of the methods under ...
Näf, Jeffrey +3 more
openaire +3 more sources
Multiple Imputation Ensembles (MIE) for dealing with missing data [PDF]
, 2020 Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation ...
A Farhangfar +49 more
core +1 more source
To Impute or not to Impute? Missing Data in Treatment Effect Estimation
CoRR, 2022 Missing data is a systemic problem in practical scenarios that causes noise and bias when estimating treatment effects. This makes treatment effect estimation from data with missingness a particularly tricky endeavour. A key reason for this is that standard assumptions on missingness are rendered insufficient due to the presence of an additional ...
Jeroen Berrevoets +4 more
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Metabolites, 2020 To ensure scientific reproducibility of metabolomics data, alternative statistical methods are needed. A paradigm shift away from the p-value toward an embracement of uncertainty and interval estimation of a metabolite’s true effect size may lead to ...
Christopher E. Gillies +7 more
doaj +1 more source
Imputation of missing genotypes: an empirical evaluation of IMPUTE [PDF]
BMC Genetics, 2008 Abstract Background Imputation of missing genotypes is becoming a very popular solution for synchronizing genotype data collected with different microarray platforms but the effect of ethnic background, subject ascertainment, and amount of missing data on the accuracy of imputation are not well understood.
Zhao, Zhenming +8 more
openaire +4 more sources