{"accessLevel": "public", "bureauCode": ["020:00"], "contactPoint": {"fn": "Holly Mortensen", "hasEmail": "mailto:mortensen.holly@epa.gov"}, "description": "This ScienceHub entry provides Associated Data (Supplementary data 1) from the published manuscript Comput Toxicol. 2023 Jan 25;25:100261. doi: 10.1016/j.comtox.2023.100261. Further availability of data, as stated in the manuscript, will be made available on request. \n\nThis dataset is associated with the following publication:\nRomano, J., L. Mei, J. Senn, J. Moore, and H. Mortensen. Exploring genetic influences on adverse outcome pathways using heuristic simulation and graph data science.   Computational Toxicology. Elsevier B.V., Amsterdam,  NETHERLANDS, 25: 100261, (2023).", "distribution": [{"downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1532078/NIHMS1933008-supplement-Supplementary_data_1%20%281%29.docx", "mediaType": "application/vnd.openxmlformats-officedocument.wordprocessingml.document", "title": "NIHMS1933008-supplement-Supplementary_data_1 (1).docx"}], "identifier": "https://doi.org/10.23719/1532078", "keyword": ["liver cancer", "genetic programming", "Graph data science", "adverse outcome pathway"], "license": "https://pasteur.epa.gov/license/sciencehub-license-non-epa-generated.html", "modified": "2023-01-25", "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.2023.100261", "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10569310", "https://pmc.ncbi.nlm.nih.gov/articles/instance/10569310/bin/NIHMS1933008-supplement-Supplementary_data_1.docx"], "rights": null, "title": "NIHMS1933008-supplement-Supplementary_data_1 (1)"}