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A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data
Supplementary data for "Tia Tate, Grace Patlewicz, Imran Shah,
A 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.".
This dataset is associated with the following publication:
Tate, 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).
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| 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, A 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.". This dataset is associated with the following publication: Tate, 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 |
[ { "title": "1-s2.0-S2468111324000033-mmc1.zip", "mediaType": "application/zip", "downloadURL": "https://pasteur.epa.gov/uploads/10.23719/1530883/1-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 |
