References

References

CoPSAC (2021) Code of Practice for Scientific Advisory Committees and Councils: CoPSAC 2021 - GOV.UK.

Kalian, A.D., Benfenati, E., Osborne, O.J., Dorne, J.L.C., Gott, D., Potter, C., Guo, M. and Hogstrand, C., 2023. Improving accuracy scores of neural network driven QSAR models of mutagenicity. In Computer Aided Chemical Engineering (Vol. 52, pp. 2717-2722). Elsevier. https://doi.org/10.1016/B978-0-443-15274-0.50432-7.

Kalian, A.D., Benfenati, E., Osborne, O.J., Gott, D., Potter, C., Dorne, J.L.C., Guo, M. and Hogstrand, C., 2023. Exploring Dimensionality Reduction Techniques for Deep Learning Driven QSAR Models of Mutagenicity. Toxics, 11(7), p.572. https://doi.org/10.3390/toxics11070572.

Kalian, A.D., Benfenati, E., Gott, D., Potter, C., Dorne, J.L., Osborne, O.J., Guo, M. and Hogstand, C., 2024. P05-37 Graph attention networks using knowledge graphs, for predicting novel points of departure for brominated flame retardants. Toxicology Letters, 399, pp.S146-S147. https://doi.org/10.1016/j.toxlet.2024.07.373.

Silva, A.D.C.E., Loizou, G.D., McNally, K., Osborne, O., Potter, C., Gott, D., Colbourne, J.K. and Viant, M.R., 2024. A novel method to derive a human safety limit for PFOA by gene expression profiling and modelling. Frontiers in toxicology, 6, p.1368320. https://doi.org/10.3389/ftox.2024.1368320.