Gene–environment interaction: A variable selection perspective
Gene–environment interactions have important implications for elucidating the genetic basis
of complex diseases beyond the joint function of multiple genetic factors and their …
of complex diseases beyond the joint function of multiple genetic factors and their …
[HTML][HTML] The relationship between occupational stress, musculoskeletal disorders and the mental health of coal miners: The interaction between BDNF gene, TPH2 …
X Li, T Jiang, X Sun, X Yong, X Ma, J Liu - Journal of Psychiatric Research, 2021 - Elsevier
Objective Mental disorders are prevalent among the population and seriously endanger
people's working ability as well as their physical and mental health. This study employed …
people's working ability as well as their physical and mental health. This study employed …
Bayesian variable selection for understanding mixtures in environmental exposures
Social and environmental stressors are crucial factors in child development. However, there
exists a multitude of measurable social and environmental factors—the effects of which may …
exists a multitude of measurable social and environmental factors—the effects of which may …
Enrank: an ensemble method to detect pulmonary hypertension biomarkers based on feature selection and machine learning models
Pulmonary hypertension (PH) is a common disease that affects the normal functioning of the
human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an …
human pulmonary arteries. The peripheral blood mononuclear cells (PMBCs) served as an …
Detection of Interaction Effects in a Nonparametric Concurrent Regression Model
R Pan, Z Wang, Y Wu - Entropy, 2023 - mdpi.com
Many methods have been developed to study nonparametric function-on-function
regression models. Nevertheless, there is a lack of model selection approach to the …
regression models. Nevertheless, there is a lack of model selection approach to the …
The Bayesian regularized quantile varying coefficient model
The quantile varying coefficient (VC) model can flexibly capture dynamical patterns of
regression coefficients. In addition, due to the quantile check loss function, it is robust …
regression coefficients. In addition, due to the quantile check loss function, it is robust …
Robust Bayesian variable selection for gene–environment interactions
Abstract Gene–environment (G× E) interactions have important implications to elucidate the
etiology of complex diseases beyond the main genetic and environmental effects. Outliers …
etiology of complex diseases beyond the main genetic and environmental effects. Outliers …
Penalized variable selection for lipid–environment interactions in a longitudinal lipidomics study
Lipid species are critical components of eukaryotic membranes. They play key roles in many
biological processes such as signal transduction, cell homeostasis, and energy storage …
biological processes such as signal transduction, cell homeostasis, and energy storage …
Gene‐gene interaction analysis incorporating network information via a structured Bayesian approach
X Qin, S Ma, M Wu - Statistics in medicine, 2021 - Wiley Online Library
Increasing evidence has shown that gene‐gene interactions have important effects in
biological processes of human diseases. Due to the high dimensionality of genetic …
biological processes of human diseases. Due to the high dimensionality of genetic …
Identifying gene–environment interactions with robust marginal Bayesian variable selection
In high-throughput genetics studies, an important aim is to identify gene–environment
interactions associated with the clinical outcomes. Recently, multiple marginal penalization …
interactions associated with the clinical outcomes. Recently, multiple marginal penalization …