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Publications

Graphing and reporting heterogeneous treatment effects through reference classes.

Watson JA. and Holmes CC., (2020), Trials, 21

Testing for dependence on tree structures.

Behr M. et al, (2020), Proceedings of the National Academy of Sciences of the United States of America

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness.

Vollmer S. et al, (2020), BMJ (Clinical research ed.), 368

Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error.

Watson JA. and Holmes CC., (2020), Trials, 21

Interferon lambda 4 impacts the genetic diversity of hepatitis C virus.

Ansari MA. et al, (2019), eLife, 8

A Cost-Effectiveness Analysis of Shortened Direct-Acting Antiviral Treatment in Genotype 1 Noncirrhotic Treatment-Naive Patients With Chronic Hepatitis C Virus.

Fawsitt CG. et al, (2019), Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 22, 693 - 703

Cost-Effectiveness Analysis of Baseline Testing for Resistance-Associated Polymorphisms to Optimize Treatment Outcome in Genotype 1 Noncirrhotic Treatment-Naïve Patients With Chronic Hepatitis C Virus

Fawsitt CG. et al, (2019), Value in Health

General Bayesian updating and the loss-likelihood bootstrap

Lyddon SP. et al, (2019), Biometrika, 106, 465 - 478

Scalable nonparametric sampling from multimodal posteriors with the posterior bootstrap

Fong E. et al, (2019), 36th International Conference on Machine Learning, ICML 2019, 2019-June, 3443 - 3464

GWAS identifies 14 loci for device-measured physical activity and sleep duration.

Doherty A. et al, (2018), Nature communications, 9

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration.

Wang Z. et al, (2018), iScience, 9, 451 - 460

Principles of Bayesian inference using general divergence criteria

Jewson J. et al, (2018), Entropy, 20

Machine learning for randomised controlled trials: identifying treatment effectheterogeneity with strict control of type I error

Holmes C. and Watson J., (2018)

High-throughput mouse phenomics for characterizing mammalian gene function.

Brown SDM. et al, (2018), Nature reviews. Genetics, 19, 357 - 370

Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.

Willetts M. et al, (2018), Scientific reports, 8

Multiscale Blind Source Separation

Behr M. et al, (2018), Annals of Statistics, 46, 711 - 744

A general framework for predicting the transcriptomic consequences of non-coding variation

Abdalla M. et al, (2018)

NOX1 loss-of-function genetic variants in patients with inflammatory bowel disease.

Schwerd T. et al, (2018), Mucosal immunology, 11, 562 - 574

Nonparametric learning from Bayesian models with randomized objective functions

Lyddon S. et al, (2018), Advances in Neural Information Processing Systems, 2018-December, 2071 - 2081

Probabilistic Boolean tensor decomposition

Rukat T. et al, (2018), 35th International Conference on Machine Learning, ICML 2018, 10, 7007 - 7020

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