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Bioinformatics (Oxford, England)|March 27, 2021
Generalized Read-Across prediction using genra-pyImran Shah, Tia Tate, Grace Patlewicz
Computational Toxicology (Amsterdam, Netherlands)|July 12, 2024
A Comparison of Machine Learning Approaches for predicting Hepatotoxicity potential using Chemical Structure and Targeted Transcriptomic DataTia Tate, Grace Patlewicz, Imran Shah
Computational Toxicology (Amsterdam, Netherlands)|June 13, 2023
Repeat-dose toxicity prediction with Generalized Read-Across (GenRA) using targeted transcriptomic data: A proof-of-concept case studyTia Tate, John Wambaugh, Grace Patlewicz, et al.
Computational Toxicology (Amsterdam, Netherlands)|September 11, 2023
Towards systematic read-across using Generalised Read-Across (GenRA)Grace Patlewicz, Imran Shah
Computational Toxicology (Amsterdam, Netherlands)|February 24, 2021
Transitioning the Generalised Read-Across approach (GenRA) to quantitative predictions: A case study using acute oral toxicity dataGeorge Helman, Imran Shah, Grace Patlewicz
Computational Toxicology (Amsterdam, Netherlands)|November 1, 2019
Extending the Generalised Read-Across approach (GenRA): A systematic analysis of the impact of physicochemical property information on read-across performanceGeorge Helman, Imran Shah, Grace Patlewicz
Computational Toxicology (Amsterdam, Netherlands)|March 12, 2026
Can graph similarity metrics be helpful for analogue identification as part of a read-across approach?Brett Hagan, Imran Shah, Grace Patlewicz
Chemical Research in Toxicology|April 10, 2024
MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism SimulatorsLouis Groff, Antony Williams, Imran Shah, et al.
Chemical Research in Toxicology|July 18, 2025
Modelling In vitro Mutagenicity Using Multi-Task Deep Learning and REACH DataPanagiotis G Karamertzanis, Mike Rasenberg, Imran Shah, et al.
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