Results 21 to 30 of about 5,004,642 (164)

Detecting univariate, bivariate, and overall effects of drug mixtures using Bayesian kernel machine regression. [PDF]

Am J Drug Alcohol Abuse
Background: Innovative analytic approaches to drug studies are needed to understand better the co-use of opioids with non-opioids among people using illicit drugs. One approach is the Bayesian kernel machine regression (BKMR), widely applied in environmental epidemiology to study exposure mixtures but has received far less attention in substance use ...
Bather JR, Han L, Bennett AS, Elliott L, Goodman MS.   +4 more
europepmc   +3 more sources

Advanced Bayesian kernel machine regression for large-scale exposome studies: Making the impossible possible. [PDF]

Innovation (Camb)
Exposome studies involve analyzing numerous exposures with complex interactions and potential collinearity, presenting challenges for conventional statistical methods. While Bayesian kernel machine regression (BKMR) has emerged as a promising solution, its widespread adoption has been hindered by high computational costs and restricted interpretability.
Guo Y, Jia H, Peng Z, Xu X, Zhang Z, Pan K, Zhou Y, Kan H, Wu Z, Liu C.   +9 more
europepmc   +3 more sources

Bayesian kernel machine regression for estimating the health effects of multi-pollutant mixtures. [PDF]

Biostatistics, 2015
Because humans are invariably exposed to complex chemical mixtures, estimating the health effects of multi-pollutant exposures is of critical concern in environmental epidemiology, and to regulatory agencies such as the U.S. Environmental Protection Agency.
Bobb JF, Valeri L, Claus Henn B, Christiani DC, Wright RO, Mazumdar M, Godleski JJ, Coull BA.   +7 more
europepmc   +4 more sources

mgpr: An R package for multivariate Gaussian process regression

SoftwareX, 2023
Gaussian process regression (GPR) is a non-parametric kernel-based machine learning method. GPR is based on Bayesian formalism, which enables the estimation of prediction uncertainty of the response variables.
Petri Varvia, Janne Räty, Petteri Packalen   +2 more
doaj   +1 more source

Bayesian Kernel Machine Regression Distributed Lag Models

ISEE Conference Abstracts, 2021
BACKGROUND AND AIM: Exposures to environmental chemicals during gestation can alter health status later in life.
Ander Wilson, Hsiao Hsien Leon Hsu, Yueh Hsiu Mathilda Chiu, Robert O. Wright, Rosalind J. Wright, Brent A. Coull   +5 more
openaire   +1 more source

Association between combined exposure to plasma heavy metals and dyslipidemia in a chinese population

Lipids in Health and Disease, 2022
Background Exposure to heavy metals in the environment is widespread, while the relationship between combined exposure to heavy metals and dyslipidemia is unclear.
Tingyu Luo, Shiyi Chen, Jiansheng Cai, Qiumei Liu, Ruoyu Gou, Xiaoting Mo, Xu Tang, Kailian He, Song Xiao, Yanfei Wei, Yinxia Lin, Shenxiang Huang, Tingjun Li, Ziqi Chen, Ruiying Li, You Li, Zhiyong Zhang   +16 more
doaj   +1 more source

Prenatal Metal Concentrations and Childhood Cardiometabolic Risk Using Bayesian Kernel Machine Regression to Assess Mixture and Interaction Effects. [PDF]

Epidemiology, 2019
Background: Trace metal concentrations may affect cardiometabolic risk, but the role of prenatal exposure is unclear. We examined (1) the relation between blood metal concentrations during pregnancy and child cardiometabolic risk factors; (2) overall effects of metals mixture (essential vs.
Kupsco A, Kioumourtzoglou MA, Just AC, Amarasiriwardena C, Estrada-Gutierrez G, Cantoral A, Sanders AP, Braun JM, Svensson K, Brennan KJM, Oken E, Wright RO, Baccarelli AA, Téllez-Rojo MM.   +13 more
europepmc   +4 more sources

Sparse kernel density construction using orthogonal forward regression with leave-one-out test score and local regularization [PDF]

, 2004
The paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability.
Hong, X., Harris, C.J., Chen, S.
core   +2 more sources

An orthogonal forward regression technique for sparse kernel density estimation [PDF]

, 2008
Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates ...
Chen, Sheng, Hong, X., Harris, Chris J.
core   +2 more sources

Prenatal exposure to metal mixture and birth weight; a Bayesian kernel machine regression analysis of two cohort studies in Japan and Iran. [PDF]

J Environ Health Sci Eng
Abstract Purpose Potentially toxic metals can directly induce various adverse effects on reproductive organs or interrupt essential metals' physiological activities. Despite intensive efforts to reduce these metals in the environment, chronic and low-level exposure remains a public health problem.
Vigeh M, Yokoyama K, Nishioka E, Shamsipour M, Matsukawa T, Yunesian M.   +5 more
europepmc   +3 more sources