Kinney, D. & Watson, D.
Causal Feature Learning for Utility-Maximizing Agents. International Conference on Probabilistic Graphical Models, pp. 257-268.
Nicholls, H., John, C., Watson, D. et al.
Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci. Frontiers in Genetics, 11, 350.
Watson, D. & Floridi, L.
The Explanation Game: A Formal Framework for Interpretable Machine Learning. Synthese.
John, C., Watson, D. et al.
M3C: Monte Carlo Reference Based Consensus Clustering. Scientific Reports, 10(1), 1816.
John, C., Watson, D. et al.
Spectrum: Fast Density-Aware Spectral Clustering for Single and Multi-Omic Data. Bioinformatics, 36(4), 1159-1166.
Watson, D.
The Rhetoric and Reality of Anthropomorphism in Artificial Intelligence. Minds & Machines, 29(3), 414-440.
Öhman, C. & Watson, D.
Are the Dead Taking Over Facebook? A Big Data Approach to the Future of Death Online. Big Data & Society, 6(1), 1-13.
O'Toole, S., Watson, D., et al.
Oncometabolite Induced Primary Cilia Loss in Pheochromocytoma. Endocrine-Related Cancer, 26(1), 165-180.
Watson, D., Krutzinna, J. et al.
Clinical Applications of Machine Learning Algorithms: Beyond the Black Box. BMJ, 346, 446-448.
Watson, D. & Wright, M.
Testing Conditional Independence in Supervised Learning Algorithms. arXiv preprint, 1901.09917.
Watson, D.
The Price of Discovery: A Model of Scientific Research Markets. In Öhman, C. & Watson, D. (Eds), The 2018 Yearbook of the Digital Ethics Lab, pp. 51-63.
Foulkes, A., Watson, D. et al.
A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis. Journal of Investigative Dermatology, 139(1), 100-107.
Watson, D. & Floridi, L.
Crowdsourced Science: Sociotechnology Epistemology in the e-Research Paradigm. Synthese, 195(2), 741-764.