biostudies-literatureSakhteman AEuropean Union’s Horizon 2020 Research and Innovation Programme2066-2069https://www.ebi.ac.uk/biostudies/studies/S-EPMC8963299biostudies-literatureUnknown38(7)<h4>Purpose</h4>Endocrine disruptors are a rising concern due to the wide array of health issues that it can cause. Although there are tools for mode of action (MoA)-based prediction of endocrine disruption (e.g. QSAR Toolbox and iSafeRat), none of them is based on toxicogenomics data. Here, we present EDTox, an R Shiny application enabling users to explore and use a computational method that we have recently published to identify and prioritize endocrine disrupting (ED) chemicals based on toxicogenomic data. The EDTox pipeline utilizes previously trained toxicogenomic-driven classifiers to make predictions on new untested compounds by using their molecular initiating events. Furthermore, the proposed R Shiny app allows users to extend the prediction systems by training and adding new clasBioinformatics (Oxford, England)EDTox: an R Shiny application to predict the endocrine disruption potential of compounds.PMC8963299825762Ghosh ASakhteman AFortino VfalseEDTox: an R Shiny application to predict the endocrine disruption potential of compounds.<h4>Purpose</h4>Endocrine disruptors are a rising concern due to the wide array of health issues that it can cause. Although there are tools for mode of action (MoA)-based prediction of endocrine disruption (e.g. QSAR Toolbox and iSafeRat), none of them is based on toxicogenomics data. Here, we present EDTox, an R Shiny application enabling users to explore and use a computational method that we have recently published to identify and prioritize endocrine disrupting (ED) chemicals based on toxicogenomic data. The EDTox pipeline utilizes previously trained toxicogenomic-driven classifiers to make predictions on new untested compounds by using their molecular initiating events. Furthermore, the proposed R Shiny app allows users to extend the prediction systems by training and adding new clas2022-01-01T00:00:00Z2022 Mar2026-04-08T18:26:00.133Z2024-11-20T08:53:25.339ZS-EPMC89632993513413610.1093/bioinformatics/btac045