{"accessLevel": "public", "bureauCode": ["020:00"], "contactPoint": {"fn": "Logan Everett", "hasEmail": "mailto:everett.logan@epa.gov"}, "description": "Modeling data and analysis scripts generated during the current study are available in the github repository: https://github.com/USEPA/CompTox-MIEML. RefChemDB is available for download as supplemental material from its original publication (PMID: 30570668). LINCS gene expression data are publicly available and accessible through the gene expression omnibus (GSE92742 and GSE70138) at https://www.ncbi.nlm.nih.gov/geo/ . \n\nThis dataset is associated with the following publication:\nBundy, J., R. Judson, A. Williams, C. Grulke, I. Shah, and L. Everett. Predicting Molecular Initiating Events Using Chemical Target Annotations and Gene Expression.   BioData Mining. BioMed Central Ltd, London,  UK, issue}: 7, (2022).", "distribution": [{"accessURL": "https://github.com/USEPA/CompTox-MIEML", "title": "https://github.com/USEPA/CompTox-MIEML"}], "identifier": "https://doi.org/10.23719/1524750", "keyword": ["molecular initiating events", "chemical safety screening", "machine learning", "high throughput transcriptomics", "binary classification", "Library of integrated cellular signatures"], "license": "https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html", "modified": "2022-03-02", "programCode": ["020:000"], "publisher": {"name": "U.S. EPA Office of Research and Development (ORD)", "subOrganizationOf": {"name": "U.S. Environmental Protection Agency", "subOrganizationOf": {"name": "U.S. Government"}}}, "references": ["https://doi.org/10.1186/s13040-022-00292-z"], "rights": null, "title": "Code for Predicting MIEs from Gene Expression and Chemical Target Labels with Machine Learning (MIEML)"}