{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Sakhteman A"],"funding":["European Union’s Horizon 2020 Research and Innovation Programme"],"pagination":["2066-2069"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8963299"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["38(7)"],"pubmed_abstract":["<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 clas"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["EDTox: an R Shiny application to predict the endocrine disruption potential of compounds."],"pmcid":["PMC8963299"],"funding_grant_id":["825762"],"pubmed_authors":["Ghosh A","Sakhteman A","Fortino V"],"additional_accession":[]},"is_claimable":false,"name":"EDTox: an R Shiny application to predict the endocrine disruption potential of compounds.","description":"<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 clas","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Mar","modification":"2026-04-08T18:26:00.133Z","creation":"2024-11-20T08:53:25.339Z"},"accession":"S-EPMC8963299","cross_references":{"pubmed":["35134136"],"doi":["10.1093/bioinformatics/btac045"]}}