{
  "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": [
    "Models",
    "chemical safety research",
    "curated data",
    "pharmacokinetics"
  ],
  "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 "
}