Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds
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 expos...
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PeerJ Inc.
2024-12-01
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| author | Wu Qiao Tong Xie Jing Lu Tinghan Jia |
| author_facet | Wu Qiao Tong Xie Jing Lu Tinghan Jia |
| author_sort | Wu Qiao |
| collection | DOAJ |
| description | 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 of this integrated approach. The best performing models were random forest (RF) and voom-based diagonal quadratic discriminant analysis (voomDQDA), both achieving 100% accuracy. Support vector machine (SVM) and voom based nearest shrunken centroids (voomNSC) showed excellent performance with 96.7% test accuracy, followed by voom-based diagonal linear discriminant analysis (voomDLDA) at 95.2%. Nearest shrunken centroids (NSC), Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) achieved 90.5% and 90.2% accuracy, respectively. K-nearest neighbors (KNN) showed the lowest accuracy at 85.7%. Conclusion This study highlights the potential of integrating co-culture systems, RNA-Seq, and machine learning to develop more accurate and comprehensive in vitro methods for skin sensitization testing. Our findings contribute to the advancement of cosmetic safety assessments, potentially reducing the reliance on animal testing. |
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| id | doaj-art-2499a3b8bfe443899bde2d65f34d9b51 |
| institution | OA Journals |
| issn | 2167-8359 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | PeerJ Inc. |
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| spelling | doaj-art-2499a3b8bfe443899bde2d65f34d9b512025-08-20T01:56:48ZengPeerJ Inc.PeerJ2167-83592024-12-0112e1867210.7717/peerj.18672Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compoundsWu QiaoTong XieJing LuTinghan JiaBackground 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 of this integrated approach. The best performing models were random forest (RF) and voom-based diagonal quadratic discriminant analysis (voomDQDA), both achieving 100% accuracy. Support vector machine (SVM) and voom based nearest shrunken centroids (voomNSC) showed excellent performance with 96.7% test accuracy, followed by voom-based diagonal linear discriminant analysis (voomDLDA) at 95.2%. Nearest shrunken centroids (NSC), Poisson linear discriminant analysis (PLDA) and negative binomial linear discriminant analysis (NBLDA) achieved 90.5% and 90.2% accuracy, respectively. K-nearest neighbors (KNN) showed the lowest accuracy at 85.7%. Conclusion This study highlights the potential of integrating co-culture systems, RNA-Seq, and machine learning to develop more accurate and comprehensive in vitro methods for skin sensitization testing. Our findings contribute to the advancement of cosmetic safety assessments, potentially reducing the reliance on animal testing.https://peerj.com/articles/18672.pdfMachine learningSkin sensitizationCo-cultureTHP-1RNA-Seq |
| spellingShingle | Wu Qiao Tong Xie Jing Lu Tinghan Jia Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds PeerJ Machine learning Skin sensitization Co-culture THP-1 RNA-Seq |
| title | Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds |
| title_full | Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds |
| title_fullStr | Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds |
| title_full_unstemmed | Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds |
| title_short | Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds |
| title_sort | development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds |
| topic | Machine learning Skin sensitization Co-culture THP-1 RNA-Seq |
| url | https://peerj.com/articles/18672.pdf |
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