A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data
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).
Resources
| Name |
Format |
Description |
Link |
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57 |
1-s2.0-S2468111324000033-mmc1.zip |
https://pasteur.epa.gov/uploads/10.23719/1530883/1-s2.0-S2468111324000033-mmc1.zip |
Tags
- genra
- toxrefdb
- httr
- machine-learning