David S. Watson
David S. Watson
Lecturer in Artificial Intelligence, King's 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
The rhetoric and reality of anthropomorphism in artificial intelligence
D Watson
Minds & Machines 29 (3), 414-440, 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
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
M3C: Monte Carlo reference-based consensus clustering
CR John, D Watson, D Russ, K Goldmann, M Ehrenstein, C Pitzalis, ...
Scientific reports 10 (1), 1816, 2020
Are the dead taking over Facebook? A Big Data approach to the future of death online
CJ Ohman, D Watson
Big Data & Society, 2019
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
Spectrum: Fast density-aware spectral clustering for single and multi-omic data
C John, D Watson, M Barnes, C Pitzalis, M Lewis
Bioinformatics, 2019
The Explanation Game: A Formal Framework for Interpretable Machine Learning
D Watson, L Floridi
Synthese 198 (10), 9211-9242, 2020
Crowdsourced science: sociotechnical epistemology in the e-research paradigm
D Watson, L Floridi
Synthese 195 (2), 741–764, 2016
The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: what can they learn from each other?
J Mökander, P Juneja, DS Watson, L Floridi
Minds and Machines 32 (4), 751-758, 2022
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
Testing conditional independence in supervised learning algorithms
D Watson, M Wright
Machine Learning 110, 2107–2129, 2021
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
Conceptual challenges for interpretable machine learning
D Watson
Synthese 200, 1-33, 2022
Interpretable machine learning for genomics
D Watson
Human Genetics, 2021
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
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
Stochastic causal programming for bounding treatment effects
K Padh, J Zeitler, D Watson, M Kusner, R Silva, N Kilbertus
2nd Conference on Causal Learning and Reasoning, 2023
The system can't perform the operation now. Try again later.
Articles 1–20