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

IF 2.3 3区 生物学 Q2 MULTIDISCIPLINARY SCIENCES
PeerJ Pub Date : 2024-12-13 eCollection Date: 2024-01-01 DOI:10.7717/peerj.18672
Wu Qiao, Tong Xie, Jing Lu, Tinghan Jia
{"title":"Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds.","authors":"Wu Qiao, Tong Xie, Jing Lu, Tinghan Jia","doi":"10.7717/peerj.18672","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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%.</p><p><strong>Conclusion: </strong>This study highlights the potential of integrating co-culture systems, RNA-Seq, and machine learning to develop more accurate and comprehensive <i>in vitro</i> methods for skin sensitization testing. Our findings contribute to the advancement of cosmetic safety assessments, potentially reducing the reliance on animal testing.</p>","PeriodicalId":19799,"journal":{"name":"PeerJ","volume":"12 ","pages":"e18672"},"PeriodicalIF":2.3000,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648681/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PeerJ","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.7717/peerj.18672","RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

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 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.

求助全文
约1分钟内获得全文 求助全文
来源期刊
PeerJ
PeerJ MULTIDISCIPLINARY SCIENCES-
CiteScore
4.70
自引率
3.70%
发文量
1665
审稿时长
10 weeks
期刊介绍: PeerJ is an open access peer-reviewed scientific journal covering research in the biological and medical sciences. At PeerJ, authors take out a lifetime publication plan (for as little as $99) which allows them to publish articles in the journal for free, forever. PeerJ has 5 Nobel Prize Winners on the Board; they have won several industry and media awards; and they are widely recognized as being one of the most interesting recent developments in academic publishing.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信