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Bayesian inference of chemical exposures from NHANES urine biomonitoring data.

Zachary StanfieldR Woodrow SetzerVictoria HullRisa R SayreKristin K IsaacsJohn F Wambaugh
Published in: Journal of exposure science & environmental epidemiology (2022)
The methods described here have been compiled into an R package, bayesmarker, and made publicly available on GitHub. These inferred exposures, when coupled with predicted toxic doses via high throughput TK, can help aid in the identification of public health priority chemicals via risk-based bioactivity-to-exposure ratios.
Keyphrases
  • public health
  • high throughput
  • air pollution
  • single cell
  • electronic health record
  • data analysis