Machine learning methods for multi-walled carbon nanotubes (MWCNT) genotoxicity prediction.

ABSTRACT: Multi-walled carbon nanotubes (MWCNTs) are made of multiple single-walled carbon nanotubes (SWCNTs) which are nested inside one another forming concentric cylinders. These nanomaterials are widely used in industrial and biomedical applications, due to their unique physicochemical characteristics. However, previous studies have shown that exposure to MWCNTs may lead to toxicity and some of the physicochemical properties of MWCNTs can influence their toxicological profiles. In silico modelling can be applied as a faster and less costly alternative to experimental (in vivo and in vitro) testing for the hazard characterization of MWCNTs. This study aims at developing a fully validated predictive nanoinformatics model based on statistical and machine learning approaches for

SUBMITTER: Kotzabasaki M 

PROVIDER: S-EPMC9417168 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature