Gene–environment interaction: A variable selection perspective

F Zhou, J Ren, X Lu, S Ma, C Wu - Epistasis: Methods and Protocols, 2021 - Springer
Gene–environment interactions have important implications for elucidating the genetic basis
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 …

Bayesian variable selection for understanding mixtures in environmental exposures

DR Kowal, M Bravo, H Leong, A Bui… - Statistics in …, 2021 - Wiley Online Library
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 …

Enrank: an ensemble method to detect pulmonary hypertension biomarkers based on feature selection and machine learning models

X Liu, Y Zhang, C Fu, R Zhang, F Zhou - Frontiers in Genetics, 2021 - frontiersin.org
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 …

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 …

The Bayesian regularized quantile varying coefficient model

F Zhou, J Ren, S Ma, C Wu - Computational Statistics & Data Analysis, 2023 - Elsevier
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 …

Robust Bayesian variable selection for gene–environment interactions

J Ren, F Zhou, X Li, S Ma, Y Jiang, C Wu - Biometrics, 2023 - academic.oup.com
Abstract Gene–environment (G× E) interactions have important implications to elucidate the
etiology of complex diseases beyond the main genetic and environmental effects. Outliers …

Penalized variable selection for lipid–environment interactions in a longitudinal lipidomics study

F Zhou, J Ren, G Li, Y Jiang, X Li, W Wang, C Wu - Genes, 2019 - mdpi.com
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 …

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 …

Identifying gene–environment interactions with robust marginal Bayesian variable selection

X Lu, K Fan, J Ren, C Wu - Frontiers in Genetics, 2021 - frontiersin.org
In high-throughput genetics studies, an important aim is to identify gene–environment
interactions associated with the clinical outcomes. Recently, multiple marginal penalization …