Publications

Soares, I. et al. (2022) “Improving lab-of-origin prediction of genetically engineered plasmids via deep metric learning”, Nature Computational Science, 2(4), pp. 253–264.

Unsworth, H. et al. (2022) “Building an evidence standards framework for artificial intelligence-enabled digital health technologies”, The Lancet Digital Health, 4(4), pp. e216 - e217.

Crook, O. (2022) “Challenges and opportunities for Bayesian statistics in proteomics”, Journal of Proteome Research, 21(4), pp. 849–864.

Kanade, V., Rebeschini, P. and Vaskevicius, T. (2022) “Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition”, arXiv.

Ramisch, A. et al. (2022) “Dynamics of gene regulatory organization in longitudinal twin RNA-seq data”, in EUROPEAN JOURNAL OF HUMAN GENETICS, pp. 67–67.

Crook, O., Chung, C.-W. and Deane, C. (2022) “A functional Bayesian model for hydrogen-deuterium exchange mass-spectrometry”, bioRxiv.

Rudner, T. et al. (2022) “Tractable Function-Space Variational Inference in Bayesian Neural Networks”, in Advances in Neural Information Processing Systems.

Villanueva, E. et al. (2022) “A system-wide quantitative map of RNA and protein subcellular localisation dynamics”, bioRxiv.

Miao, N. et al. (2022) “ON INCORPORATING INDUCTIVE BIASES INTO VAES”, in ICLR 2022 - 10th International Conference on Learning Representations.

Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of Machine Learning Research, pp. 685–695.