Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds.

ABSTRACT:

Background

To enhance the accuracy of allergen detection in cosmetic compounds, we developed a co-culture system that combines HaCaT keratinocytes (transfected with a luciferase plasmid driven by the AKR1C2 promoter) and THP-1 cells for machine learning applications.

Methods

Following chemical exposure, cell cytotoxicity was assessed using CCK-8 to determine appropriate stimulation concentrations. RNA-Seq was subsequently employed to analyze THP-1 cells, followed by differential expression gene (DEG) analysis and weighted gene co-expression net-work analysis (WGCNA). Using two data preprocessing methods and three feature extraction techniques, we constructed and validated models with eight machine learning algorithms.

Results

Our results demonstrated the effectiveness

SUBMITTER: Qiao W 

PROVIDER: S-EPMC11648681 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature