Return to search results
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).
Complete Metadata
| 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 |
