{"accessLevel": "public", "bureauCode": ["020:00"], "contactPoint": {"fn": "Grace Patlewicz", "hasEmail": "mailto:patlewicz.grace@epa.gov"}, "description": "Supplementary data for \"Tia Tate, Grace Patlewicz, Imran Shah,\nA comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data, Computational Toxicology, Volume 29,  2024, 100301, ISSN 2468-1113, https://doi.org/10.1016/j.comtox.2024.100301.\". \n\nThis dataset is associated with the following publication:\nTate, T., G. Patlewicz, and I. Shah. A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data.   Computational Toxicology. Elsevier B.V., Amsterdam,  NETHERLANDS, 29: 100301, (2024).", "distribution": [{"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1530883/1-s2.0-S2468111324000033-mmc1.zip", "mediaType": "application/zip", "title": "1-s2.0-S2468111324000033-mmc1.zip"}], "identifier": "https://doi.org/10.23719/1530883", "keyword": ["GenRA", "HTTr", "ToxRefDB", "machine learning"], "license": "https://pasteur.epa.gov/license/sciencehub-license.html", "modified": "2024-02-04", "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/10.1016/j.comtox.2024.100301"], "rights": null, "title": "A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data"}