HExpPredict: In Vivo Exposure Prediction of Human Blood Exposome Using a Random Forest Model and Its Application in Chemical Risk Prioritization.

ABSTRACT:

Background

Due to many substances in the human exposome, there is a dearth of exposure and toxicity information available to assess potential health risks. Quantification of all trace organics in the biological fluids seems impossible and costly, regardless of the high individual exposure variability. We hypothesized that the blood concentration (CB) of organic pollutants could be predicted via their exposure and chemical properties. Developing a prediction model on the annotation of chemicals in human blood can provide new insight into the distribution and extent of exposures to a wide range of chemicals in humans.

Objectives

Our objective was to develop a machine learning (ML) model to predict blood concentrations (CBs) of chemicals and prioritize chemicals of health conce

SUBMITTER: Zhao F 

PROVIDER: S-EPMC10010393 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature