David Page
Impact in
- Health Informatics top 5%
- Artificial Intelligence top 2%
- Privacy-Preserving Technologies in Data
- Adversarial Robustness in Machine Learning
- Machine Learning in Healthcare
- Cryptography and Data Security
Papers in
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- Machine Learning in Healthcare 11
- Bayesian Modeling and Causal Inference 9
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- Biomedical Text Mining and Ontologies 18
- Gene expression and cancer classification 6
- Co-authors
- Steven J. Darnell (1 shared paper)Éric Lantz (1 shared paper)Julie C. Mitchell (1 shared paper)Somesh Jha (1 shared paper)Thomas Ristenpart (1 shared paper)Simon Lin (1 shared paper)Matt Fredrikson (1 shared paper)Peggy Peissig (19 shared papers)
- Journals
- Bioinformatics (4 papers)Machine Learning (2 papers)Journal of Biomedical Informatics (2 papers)Journal of Machine Learning Research (2 papers)European Journal of Human Genetics (1 paper)
- Partner nations
- United StatesPortugalUnited Kingdom
In The Last Decade
David Page
86 papers receiving 1.7k citations
David Page's Hit Papers
Peers
Comparison fields: 5 of 154
- Health Informatics 42
- Artificial Intelligence 766
- Health Information Management 102
- Computational Theory and Mathematics 188
- Toxicology 39
Countries citing papers authored by David Page
This map shows the geographic impact of David Page's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by David Page with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Page more than expected).
Fields of papers citing papers by David Page
This network shows the impact of papers produced by David Page. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by David Page. The network helps show where David Page may publish in the future.
Co-authors
The 25 scholars most cited alongside David Page, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 90 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. Hit paper breakdown → | 2014 | 313 |
| 2 | 2007 | 156 | |
| 3 | 2013 | 96 | |
| 4 | 1998 | 73 | |
| 5 | A probabilistic learning approach to whole-genome operon prediction. | 2000 | 53 |
| 6 | 2018 | 51 | |
| 7 | 2012 | 49 | |
| 8 | 2009 | 49 | |
| 9 | 2014 | 44 | |
| 10 | 2018 | 42 | |
| 11 | 2014 | 35 | |
| 12 | 2019 | 35 | |
| 13 | Relational Data Mining with Inductive Logic Programming for Link Discovery | 2002 | 34 |
| 14 | 2012 | 34 | |
| 15 | 2008 | 33 | |
| 16 | 2017 | 32 | |
| 17 | 2019 | 30 | |
| 18 | 2014 | 28 | |
| 19 | View learning for statistical relational learning: with an application to mammography | 2005 | 27 |
| 20 | 2019 | 26 |
About David Page
David Page is a scholar working on Artificial Intelligence, Molecular Biology, Computational Theory and Mathematics, Genetics and Information Systems, having authored 90 papers that have together received 1.8k indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (18 papers), Computational Drug Discovery Methods (11 papers), Machine Learning in Healthcare (11 papers), Genetic Associations and Epidemiology (9 papers), Bayesian Modeling and Causal Inference (9 papers), Data Mining Algorithms and Applications (9 papers), Pharmacovigilance and Adverse Drug Reactions (7 papers) and Gene expression and cancer classification (6 papers). The work is most often cited by research in Health Informatics (42 citations), Artificial Intelligence (766 citations), Health Information Management (102 citations), Computational Theory and Mathematics (188 citations) and Toxicology (39 citations). David Page has collaborated with scholars based in United States, Portugal and United Kingdom. Frequent co-authors include Steven J. Darnell, Éric Lantz, Julie C. Mitchell, Somesh Jha, Thomas Ristenpart, Simon Lin, Matt Fredrikson, Peggy Peissig, Elizabeth S. Burnside and Jude Shavlik. Their work appears in journals such as Bioinformatics, Machine Learning, Journal of Biomedical Informatics, Journal of Machine Learning Research and European Journal of Human Genetics.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.