Results 11 to 20 of about 216,838 (308)
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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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
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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
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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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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
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Approaching Genetics Through the MHC Lens: Tools and Methods for HLA Research
Frontiers in Genetics, 2021 The current SARS-CoV-2 pandemic era launched an immediate and broad response of the research community with studies both about the virus and host genetics.
Venceslas Douillard +8 more
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Frontiers in Genetics, 2019 Imputation of high-density genotypes to whole-genome sequences (WGS) is a cost-effective method to increase the density of available markers within a population.
Adrien M. Butty +9 more
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IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2023 Cardiovascular disease (CVD) is the leading cause of death worldwide. Primary prevention is by early prediction of the disease onset. Using laboratory data from the National Health and Nutrition Examination Survey (NHANES) in 2017-2020 timeframe (N= 7 ...
Fadlan Hamid Alfebi, Mila Desi Anasanti
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A ‘Chief Error’ of Protestant Soteriology: Sin in the Justified and Early Modern Catholic Theology
Perichoresis: The Theological Journal of Emanuel University, 2020 Catholic theologians after Trent saw the Protestant teaching about the remnants of original sin in the justified as one of the ‘chief ’ errors of Protestant soteriology.
Gaetano Matthew T.
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To Impute or Not To Impute in Untargeted Metabolomics─That is the Compositional Question [PDF]
Journal of the American Society for Mass SpectrometryABSTRACT Untargeted metabolomics often produce large datasets with missing values, arising from biological or technical factors, which can undermine statistical analyses and lead to biased biological interpretations. Imputation methods, such as k-Nearest Neighbors (kNN) and Random Forest (RF) regression are commonly used but their ...
Dennis Dimitri Krutkin +6 more
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