{"accessLevel": "public", "bureauCode": ["020:00"], "contactPoint": {"fn": "Grace Patlewicz", "hasEmail": "mailto:patlewicz.grace@epa.gov"}, "description": "Read-across (RAX) is a widely used data gap filling approach and the authors have developed a data-driven tool, called GenRA, to support expert-driven RAX. This work describes a stand-alone Python 3 package, called genra-py, which enables end-users to conduct hazard identification and point of departure (POD) estimation using GenRA. \n\nThis dataset is associated with the following publication:\nShah, I., T. Tate, and G. Patlewicz. Generalised Read-Across Prediction using genra-py.   BIOINFORMATICS. Oxford University Press, Cary, NC, USA, 37(19): 3380-3381, (2021).", "distribution": [{"accessURL": "https://github.com/i-shah/genra-py", "title": "https://github.com/i-shah/genra-py"}], "identifier": "https://doi.org/10.23719/1520471", "keyword": ["chemical safety research", "pharmacokinetics", "curated data", "Models"], "license": "https://pasteur.epa.gov/license/sciencehub-license.html", "modified": "2020-12-14", "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/10.1093/bioinformatics/btab210"], "rights": null, "title": "Generalised Read-Across Prediction using genra-py "}