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Search results (144)

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Untangling biological complexity: A deep learning approach to separating multiple signals in single-cell data.

Yau C., (2026), Cell genomics, 6

Data from Oxford Classic–Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions

Rai L. et al, (2026)

Supplementary Table S1 from Oxford Classic–Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions

Rai L. et al, (2026)

Supplementary Figure S1 from Oxford Classic–Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions

Rai L. et al, (2026)

Supplementary Figure S3 from Oxford Classic–Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions

Rai L. et al, (2026)

Supplementary Figure S5 from Oxford Classic–Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions

Rai L. et al, (2026)

Supplementary Figure S4 from Oxford Classic–Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions

Rai L. et al, (2026)

Supplementary Figure S2 from Oxford Classic–Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions

Rai L. et al, (2026)

Oxford Classic-Defined EMT Risk Stratification of High-Grade Serous Ovarian Cancer for Guiding Treatment Decisions.

Rai L. et al, (2026), Clinical cancer research : an official journal of the American Association for Cancer Research, 32, 188 - 202

Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors

Wambua S. et al, (2025), BMC Medicine, 23

araCNA: somatic copy number profiling using long-range sequence models.

Visscher E. and Yau C., (2025), NAR genomics and bioinformatics, 7

Development and validation of a postpartum cardiovascular disease risk prediction model in women incorporating reproductive and pregnancy-related predictors.

Wambua S. et al, (2025), BMC medicine, 23

GPerturb: Gaussian process modelling of single-cell perturbation data.

Xing H. and Yau C., (2025), Nature communications, 16

Incorporation of Patient and Public Involvement in Statistical Methodology Research: Summary of Workshop Proceedings.

Smith A. et al, (2025), Statistics in medicine, 44

Refined selection of individuals for preventive cardiovascular disease treatment with a transformer-based risk model.

Rao S. et al, (2025), The Lancet. Digital health, 7

Continual learning via probabilistic exchangeable sequence modelling

Xing H. and Yau C., (2025), Transactions on Machine Learning Research, 2025-October

eP117 The Accuracy of Clinical Coding in Electronic Patient Records Systems in Emergency General Surgery

Yasmin A. et al, (2025), BJS-BRITISH JOURNAL OF SURGERY, 112

Machine Learning in Clinical Decision Making in Emergency General Surgery: A Qualitative Study of the Acceptability of Artificial Intelligence (MAIDEN-Q)

Chan A. et al, (2025), BJS-BRITISH JOURNAL OF SURGERY, 112

O1 Using Machine Learning to predict the Severity of Acute Pancreatitis: A Systematic Review

Nadeem Z. et al, (2025), BJS-BRITISH JOURNAL OF SURGERY, 112

Author Correction: Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines.

Martindale APL. et al, (2024), Nature communications, 15

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