Individual variations in drug efficacy, side effects and adverse drug reactions are still
challenging that cannot be ignored in drug research and development. The aim of …

Large-scale microbiome studies are progressively utilizing multiomics designs, which
include the collection of microbiome samples together with host genomics and …

In the field of data analysis, it is often faced with a large number of missing values, especially
in metabolomics data, this problem is more prominent. Data imputation is a common method …

Background Wheeze and allergic sensitization are the strongest early-life predictors of
childhood asthma development; the molecular origins of these early-life phenotypes are …

Background Integration of metabolomics with genetics may advance understanding of
disease pathogenesis but has been underused in asthma genetic studies. Objective We …

Out of the thousands of metabolites in a given specimen, most metabolomics experiments
measure only hundreds, with poor overlap across experimental platforms. Here, we describe …

We study high-dimensional, ridge-regularized logistic regression in a setting in which the
covariates may be missing or corrupted by additive noise. When both the covariates and the …

We study the effects of missingness on the estimation of population parameters. Moving
beyond restrictive missing completely at random (MCAR) assumptions, we first formulate a …

Vitamin D deficiency contributes to a multitude of health conditions, but its biological
mechanisms are not adequately understood. Untargeted metabolomics offers the …

The recent explosion of genetic and high dimensional biobank and'omic'data has provided
researchers with the opportunity to investigate the shared genetic origin (pleiotropy) of …