Ensemble learning for predicting ex vivo human placental barrier permeability.

ABSTRACT:

Background

The placental barrier protects the fetus from exposure to some toxicants and is vital for drug development and risk assessment of environmental chemicals. However, in vivo experiments for assessing the placental barrier permeability of chemicals is not ethically acceptable. Although ex vivo placental perfusion methods provide good alternatives for the assessment of placental barrier permeability, the application to a large number of test chemicals could be time- and resource-consuming. Computational prediction models for ex vivo placental barrier permeability are therefore desirable.

Methods

A total of 87 chemicals and corresponding 1444 physicochemical properties were divided into training and test datasets. Three types of algorithms including linear regression,

SUBMITTER: Chou CY 

PROVIDER: S-EPMC9502578 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature