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A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data
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United State Environmental Protection Agency - view all
Update frequencyunknown
Last updated7 days ago
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Overview

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).

GenRAHTTrToxRefDBmachine learning
Additional Information
KeyValue
Dcat Modified2024-02-04
Dcat Publisher NameU.S. EPA Office of Research and Development (ORD)
Guidhttps://doi.org/10.23719/1530883
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  • 1-s2.0-S2468111324000033-mmc1.zip