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Publications

Goto, A. et al. (2022) “Understanding the genetics of viral drug resistance by integrating clinical data and mining of the scientific literature”, Scientific Reports, 12.
Nicholson, G. et al. (2022) “Multivariate phenotype analysis enables genome-wide inference of mammalian gene function”, PLOS Biology, 20(8), pp. e3001723 - e3001723-.
Nicholson, G. et al. (2022) “Multivariate phenotype analysis enables genome-wide inference of mammalian gene function”, PLOS Biology, 20(8), pp. e3001723 - e3001723-.
Jersakova, R. et al. (2022) “Bayesian Imputation of COVID-19 Positive Test Counts for Nowcasting Under Reporting Lag”, Journal of the Royal Statistical Society Series C (Applied Statistics), 71(4), pp. 834–860.
Jersakova, R. et al. (2022) “Bayesian Imputation of COVID-19 Positive Test Counts for Nowcasting Under Reporting Lag”, Journal of the Royal Statistical Society Series C (Applied Statistics), 71(4), pp. 834–860.
Meli, R., Morris, G. and Biggin, P. (2022) “Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review”, Frontiers in Bioinformatics, 2.
Pardo-Diaz, J. et al. (2022) “Generating weighted and thresholded gene coexpression networks using signed distance correlation.”, Network Science, 10(2), pp. 131–145.
Benedetto, U. et al. (2022) “Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis”, Journal of Thoracic and Cardiovascular Surgery, 163(Int Stat Rev 80 2012), pp. 2075 – 2087.e9.
Campbell, A. et al. (2022) “Online variational filtering and parameter learning”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, pp. 18633–18645.
Campbell, A. et al. (2022) “A Continuous Time Framework for Discrete Denoising Models”, arXiv.