[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118554-en":3,"doc-seo-118554-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118554,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine learning classification of steatogenic compounds using toxicogenomics profiles","New approach methodologies for toxicity testing are driving computational models that use transcriptomic signals to forecast chemical-induced harm. This study applied supervised machine learning to gene expression data from primary human hepatocytes and rat liver models in vitro and in vivo to predict drug-induced hepatic steatosis. Five classifiers were assessed using Open TG-GATEs microarray data. Support vector machine achieved the best performance across species, and top predictive genes were profiled to reveal lipid metabolism, mitochondrial function, insulin signaling, and oxidative stress pathways.","Journal Pre-proof  \nMachine learning classification of steatogenic compounds using toxicogenomics profiles  \nBrian Bwanya, Saad Lodhi, Theo M de Kok, Luiz Ladeira, Marcha CT Verheijen, Danyel GJ Jennen, Florian Caiment  \nPII: S0300-483X(25)00196-9  \nDOI: [https://doi.org/10.1016/j.tox.2025.154237](https://doi.org/10.1016/j.tox.2025.154237)  \n[Reference: TOX154237](Reference: TOX154237)  \n[To appear in:](To appear in: Toxicology)[ Toxicology](To appear in: Toxicology)  \n[Received date: 10 June 2025](Received date: 10 June 2025)  \n[Revised date: 14 July 2025](Revised date: 14 July 2025)  \n[Accepted date: 14 July 2025](Accepted date: 14 July 2025)  \nPlease cite this article as: Brian Bwanya, Saad Lodhi, Theo M de Kok, Luiz Ladeira, Marcha CT Verheijen, Danyel GJ Jennen and Florian Caiment, Machine learning classification of steatogenic compounds using toxicogenomics profiles, Toxicology, (2025) doi:[https://doi.org/10.1016/j.tox.2025.154237](https://doi.org/10.1016/j.tox.2025.154237)  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2025 Published by Elsevier.  \nMachine learning classification of steatogenic compounds using  \ntoxicogenomics profiles  \nBrian Bwanyaa*, Saad Lodhia*, Theo M de Koka, Luiz Ladeirab, Marcha CT Verheijena, Danyel GJ Jennena, and Florian Caimenta  \n* Contributed equally  \na Department of Translational Genomics, GROW Research Institute for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, The Netherlands. b Biomechanics Research Unit, GIGA Institute, University of Liège, Avenue de l'Hôpital, 11, B34 +5 4000 Liège Belgium.  \nCorresponding author: Florian Caiment  \nEmail address: [florian.caiment@maastrichtuniversity.nl](florian.caiment@maastrichtuniversity.nl)  \nAbstract  \nThe transition toward new approach methodologies for toxicity testing has accelerated the development of computational models that utilize transcriptomic data to predict chemical-induced adverse effects. Here, we applied supervised machine learning to gene expression data derived from primary human hepatocytes and rat liver models (in vitro and in vivo) to predict drug-induced hepatic steatosis. We evaluated five machine learning classifiers using microarray data from the Open TG-GATEs database. Among these, support vector machine (SVM) consistently achieved the highest performance, with area under the receiver operating characteristic curve (ROC-AUC) of 0.820 in primary human hepatocytes, 0.975 in the rat in vitro model, and 0.966 in the rat in vivo model. To gain mechanistic insights, we functionally profiled the top-ranked predictive genes. Enrichment analyses revealed strong associations with lipid metabolism, mitochondrial function, insulin signalling, oxidative stress, all biological processes central to steatosis pathogenesis. Key predictive genes such as CYP1A1, PLIN2, and GCK mapped to lipid metabolism networks and liver disease annotations, while others highlighted novel transcriptomics signals. Integration with differentially expressed genes and known steatosis markers highlighted both overlapping and distinct molecular features, suggesting that machine learning models capture biologically relevant signals. These findings demonstrate the potential of machine learning models guided by transcriptomic data to identify early molecular signatures of drug-induced hepatic steatosis. The  \nsupport vector machine model ’s strong predictive accuracy across species highlights its promise ","cbCairPzy7qXMSwR","https://ap.wps.com/l/cbCairPzy7qXMSwR","pdf",1576876,1,40,"English","en",105,"# Abstract\n# Highlights\n# 1. Introduction","[{\"question\":\"What data and models were used to predict drug-induced hepatic steatosis?\",\"answer\":\"Supervised machine learning was applied to gene expression data from primary human hepatocytes and rat liver models (in vitro and in vivo). Five classifiers were evaluated using microarray data from the Open TG-GATEs database.\"},{\"question\":\"Which machine learning classifier performed best and how was it evaluated?\",\"answer\":\"The support vector machine (SVM) classifier showed the highest performance. Reported ROC-AUC values were 0.820 for primary human hepatocytes, 0.975 for the rat in vitro model, and 0.966 for the rat in vivo model.\"},{\"question\":\"How did the study interpret the mechanistic basis of prediction?\",\"answer\":\"Functionally profiling top-ranked predictive genes and performing enrichment analyses linked them to lipid metabolism, mitochondrial function, insulin signaling, and oxidative stress. Predictive genes such as CYP1A1, PLIN2, and GCK mapped to lipid metabolism networks and liver disease annotations.\"}]","Machine learning classification of steatogenic compounds using toxicogenomics profiles | PDF",1785684127,101,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-classification-of-steatogenic-compounds-using-toxicogenomics-profiles","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-classification-of-steatogenic-compounds-using-toxicogenomics-profiles/118554/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What data and models were used to predict drug-induced hepatic steatosis?","Question",{"text":75,"@type":76},"Supervised machine learning was applied to gene expression data from primary human hepatocytes and rat liver models (in vitro and in vivo). Five classifiers were evaluated using microarray data from the Open TG-GATEs database.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning classifier performed best and how was it evaluated?",{"text":80,"@type":76},"The support vector machine (SVM) classifier showed the highest performance. Reported ROC-AUC values were 0.820 for primary human hepatocytes, 0.975 for the rat in vitro model, and 0.966 for the rat in vivo model.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the study interpret the mechanistic basis of prediction?",{"text":84,"@type":76},"Functionally profiling top-ranked predictive genes and performing enrichment analyses linked them to lipid metabolism, mitochondrial function, insulin signaling, and oxidative stress. Predictive genes such as CYP1A1, PLIN2, and GCK mapped to lipid metabolism networks and liver disease annotations.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":21,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]