{
  "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": [
    "Library of integrated cellular signatures",
    "binary classification",
    "chemical safety screening",
    "high throughput transcriptomics",
    "machine learning",
    "molecular initiating events"
  ],
  "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)"
}