David S. Watson
David S. Watson
Postdoctoral Research Fellow, University College London
Verified email at - Homepage
Cited by
Cited by
Clinical applications of machine learning: Beyond the black box
D Watson, J Krutzinna, IN Bruce, CEM Griffiths, IB McInnes, MR Barnes, ...
The British Medical Journal 364, 2019
Molecular Portraits of Early Rheumatoid Arthritis Identify Clinical and Treatment Response Phenotypes
M Lewis, M Barnes, K Blighe, K Goldmann, S Rana, J Hackney, ...
Cell Reports 28 (9), 2019
Signatures of inflammation and impending multiple organ dysfunction in the hyperacute phase of trauma: A prospective cohort study
CP Cabrera, J Manson, JM Shepherd, HD Torrance, D Watson, ...
PLoS medicine 14 (7), e1002352, 2017
The rhetoric and reality of anthropomorphism in artificial intelligence
D Watson
Minds & Machines, 2019
Are the dead taking over Facebook? A Big Data approach to the future of death online
CJ Ohman, D Watson
Big Data & Society, 2019
Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci
HL Nicholls, CR John, DS Watson, PB Munroe, MR Barnes, CP Cabrera
Frontiers in Genetics 11, 350, 2020
Crowdsourced science: sociotechnical epistemology in the e-research paradigm
D Watson, L Floridi
Synthese 195 (2), 741–764, 2016
Sex differences in the nitrate-nitrite-NO• pathway: Role of oral nitrate-reducing bacteria
V Kapil, KS Rathod, RS Khambata, M Bahra, S Velmurugan, A Purba, ...
Free Radical Biology and Medicine 126, 113-121, 2018
The Explanation Game: A Formal Framework for Interpretable Machine Learning
D Watson, L Floridi
Synthese 198 (10), 9211-9242, 2020
M3C: Monte Carlo reference-based consensus clustering
CR John, D Watson, D Russ, K Goldmann, M Ehrenstein, C Pitzalis, ...
Scientific Reports 10 (1), 1-14, 2020
Spectrum: Fast density-aware spectral clustering for single and multi-omic data
C John, D Watson, M Barnes, C Pitzalis, M Lewis
Bioinformatics, 2019
A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis
AC Foulkes, DS Watson, DF Carr, JG Kenny, T Slidel, R Parslew, ...
Journal of Investigative Dermatology 139 (1), 100-107, 2019
Testing conditional independence in supervised learning algorithms
D Watson, M Wright
Machine Learning 110, 2107–2129, 2021
The RA-MAP Consortium: a working model for academia–industry collaboration
AP Cope, MR Barnes, A Belson, M Binks, S Brockbank, ...
Nature Reviews Rheumatology 14 (1), 53, 2018
Research techniques made simple: bioinformatics for genome-scale biology
AC Foulkes, DS Watson, CEM Griffiths, RB Warren, W Huber, MR Barnes
Journal of Investigative Dermatology 137 (9), e163-e168, 2017
Local Explanations via Necessity and Sufficiency: Unifying Theory and Practice
D Watson, L Gultchin, A Taly, L Floridi
Proceedings of the 37th Conference on Uncertainty in Artificial Intelligence, 2021
Interpretable machine learning for genomics
D Watson
Human Genetics, 2021
Oncometabolite induced primary cilia loss in pheochromocytoma
SM O’Toole, DS Watson, TV Novoselova, LEL Romano, PJ King, ...
Endocrine-related cancer 26 (1), 165-180, 2019
Conceptual challenges for interpretable machine learning
D Watson
Synthese 200, 1-33, 2022
Causal feature learning for utility-maximizing agents
D Kinney, D Watson
International Conference on Probabilistic Graphical Models 138, 257-268, 2020
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