Daniel Silk
Impact in
- Statistics and Probability top 10%
- Markov Chains and Monte Carlo Methods
Papers in
-
- Gene Regulatory Network Analysis 3
- Microbial Metabolic Engineering and Bioproduction 2
- Genetics 3
- Bacterial Genetics and Biotechnology 2
- Evolution and Genetic Dynamics 1
- Co-authors
- Michael P. H. Stumpf (5 shared papers)C. Barnes (4 shared papers)Xia Sheng (1 shared paper)Tina Toni (2 shared papers)Paul Kirk (2 shared papers)Sarah Filippi (1 shared paper)Simon Moon (1 shared paper)Margaret J. Dallman (1 shared paper)
- Journals
- Nature Communications (1 paper)Proceedings of the National Academy of Sciences (1 paper)Interface Focus (1 paper)PLoS Computational Biology (1 paper)Statistical Applications in Genetics and Molecular Biology (1 paper)
- Partner nations
- United KingdomUnited States
In The Last Decade
Daniel Silk
6 papers receiving 190 citations
Peers
Comparison fields: 5 of 55
- Statistics and Probability 32
- Computational Mathematics 2
- Molecular Biology 122
- Modeling and Simulation 8
- Biophysics 10
Countries citing papers authored by Daniel Silk
This map shows the geographic impact of Daniel Silk'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 Daniel Silk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Silk more than expected).
Fields of papers citing papers by Daniel Silk
This network shows the impact of papers produced by Daniel Silk. 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 Daniel Silk. The network helps show where Daniel Silk may publish in the future.
Co-authors
The 9 scholars most cited alongside Daniel Silk, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2011 | 64 | |
| 2 | 2014 | 41 | |
| 3 | 2011 | 40 | |
| 4 | 2013 | 24 | |
| 5 | 2011 | 20 | |
| 6 | Preventing Ideological Violence: Communities, Police and Case Studies of “Success” | 2013 | 2 |
About Daniel Silk
Daniel Silk is a scholar working on Molecular Biology, Genetics, Artificial Intelligence, Statistical and Nonlinear Physics and Cognitive Neuroscience, having authored 6 papers that have together received 191 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (3 papers), Microbial Metabolic Engineering and Bioproduction (2 papers), Bacterial Genetics and Biotechnology (2 papers), Evolution and Genetic Dynamics (1 paper), Markov Chains and Monte Carlo Methods (1 paper), Neural dynamics and brain function (1 paper), Model Reduction and Neural Networks (1 paper) and Neural Networks and Applications (1 paper). The work is most often cited by research in Statistics and Probability (32 citations), Computational Mathematics (2 citations), Molecular Biology (122 citations), Modeling and Simulation (8 citations) and Biophysics (10 citations). Daniel Silk has collaborated with scholars based in United Kingdom and United States. Frequent co-authors include Michael P. H. Stumpf, C. Barnes, Xia Sheng, Tina Toni, Paul Kirk, Sarah Filippi, Simon Moon, Margaret J. Dallman and Basia Spalek. Their work appears in journals such as Nature Communications, Proceedings of the National Academy of Sciences, Interface Focus, PLoS Computational Biology and Statistical Applications in Genetics and Molecular Biology.
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.