Results 61 to 70 of about 1,716,816 (278)
FEBS Letters, EarlyView.The Ile181Asn variant of human UDP‐xylose synthase (hUXS1), associated with a short‐stature genetic syndrome, has previously been reported as inactive. Our findings demonstrate that Ile181Asn‐hUXS1 retains catalytic activity similar to the wild‐type but exhibits reduced stability, a looser oligomeric state, and an increased tendency to precipitate ...
Tuo Li +2 more
wiley +1 more source
On testing the missing at random assumption [PDF]
, 2006 Most approaches to learning from incomplete data are based on the assumptionthat unobserved values are missing at random (mar). While the mar assumption, as such, is not testable, it can become testable in the context of other distributional assumptions, e.g. the naive Bayes assumption.
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Using item response theory as a methodology to impute categorical missing values
Scientific ReportsMost datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences that can be made from the data.
Adrienne Kline, Yuan Luo
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Role of survey response rates on valid inference: an application to HIV prevalence estimates
Emerging Themes in Epidemiology, 2018 Background Nationally-representative surveys suggest that females have a higher prevalence of HIV than males in most African countries. Unfortunately, these results are made on the basis of surveys with non-ignorable missing data.
Miguel Marino, Marcello Pagano
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A survey on missing data in machine learning
Journal of Big Data, 2021 Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or ...
Tlamelo Emmanuel +5 more
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Variational Bayesian Inference for Quantile Regression Models with Nonignorable Missing Data
Mathematics, 2023 Quantile regression models are remarkable structures for conducting regression analyses when the data are subject to missingness. Missing values occur because of various factors like missing completely at random, missing at random, or missing not at ...
Xiaoning Li, Mulati Tuerde, Xijian Hu
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Additive hazards regression with censoring indicators missing at random [PDF]
Canadian Journal of Statistics, 2010 AbstractIn this article, the authors consider a semiparametric additive hazards regression model for right‐censored data that allows some censoring indicators to be missing at random. They develop a class of estimating equations and use an inverse probability weighted approach to estimate the regression parameters.
Song, Xinyuan +3 more
openaire +3 more sources
Cell wall target fragment discovery using a low‐cost, minimal fragment library
FEBS Letters, EarlyView.LoCoFrag100 is a fragment library made up of 100 different compounds. Similarity between the fragments is minimized and 10 different fragments are mixed into a single cocktail, which is soaked to protein crystals. These crystals are analysed by X‐ray crystallography, revealing the binding modes of the bound fragment ligands.
Kaizhou Yan +5 more
wiley +1 more source
FEBS Letters, EarlyView.In this study, we found that human cervical‐derived adipocytes maintain intracellular iron level by regulating the expression of iron transport‐related proteins during adrenergic stimulation. Melanotransferrin is predicted to interact with transferrin receptor 1 based on in silico analysis.
Rahaf Alrifai +9 more
wiley +1 more source
Two-stage multiple imputation with a longitudinal composite variable
BMC Medical Research MethodologyBackground Missing data are common in longitudinal studies. Multiple imputation (MI) is widely used to handle missing data. However, most of the MI methods assume various missing data types as missing at random (MAR) in imputation.
Xuzhi Wang, Martin G. Larson, Chunyu Liu
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