Enoch Yeung
- Molecular Biology
- Genetics
- Biomedical Engineering
- Statistical and Nonlinear Physics top 10%
- Control and Systems Engineering top 10%
- Co-authors
- Richard M. MurrayZachary Z. SunVincent NoireauxClarmyra A. HayesSebastian J. MaerklHenrike NiederholtmeyerYutaka HoriSean Warnick
- Topics
- Gene Regulatory Network Analysis (21 papers)Model Reduction and Neural Networks (7 papers)Microbial Metabolic Engineering and Bioproduction (6 papers)
- Partner nations
- United StatesUnited KingdomLuxembourg
In The Last Decade
Enoch Yeung
31 papers receiving 770 citations
Peers
Comparison fields: 5 of 82
- Molecular Biology 615
- Genetics 138
- Biomedical Engineering 95
- Statistical and Nonlinear Physics 75
- Control and Systems Engineering 66
Countries citing papers authored by Enoch Yeung
This map shows the geographic impact of Enoch Yeung'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 Enoch Yeung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Enoch Yeung more than expected).
Fields of papers citing papers by Enoch Yeung
This network shows the impact of papers produced by Enoch Yeung. 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 Enoch Yeung. The network helps show where Enoch Yeung may publish in the future.
Co-authorship network of co-authors of Enoch Yeung
This figure shows the co-authorship network connecting the top 25 collaborators of Enoch Yeung. A scholar is included among the top collaborators of Enoch Yeung based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Enoch Yeung. Enoch Yeung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 10 | |
| 3 | 4 | |
| 4 | 5 | |
| 5 | 6 | |
| 6 | 10 | |
| 7 | 71 | |
| 8 | A data-driven method for quantifying the impact of a genetic circuit on its host | 8 |
| 9 | 12 | |
| 10 | 6 | |
| 11 | 2 | |
| 12 | 0 | |
| 13 | 86 | |
| 14 | 173 | |
| 15 | 9 | |
| 16 | 9 | |
| 17 | 12 | |
| 18 | 10 | |
| 19 | 32 | |
| 20 | A Comparison of Network Reconstruction Methods for Chemical Reaction Networks | 4 |
About Enoch Yeung
Enoch Yeung is a scholar working on Statistical and Nonlinear Physics, Biophysics and Statistics, Probability and Uncertainty, having authored 32 papers that have together received 780 indexed citations. Recurring topics across this work include Gene Regulatory Network Analysis (21 papers), Model Reduction and Neural Networks (7 papers) and Microbial Metabolic Engineering and Bioproduction (6 papers). The work is most often cited by research in Molecular Biology (615 citations), Statistical and Nonlinear Physics (75 citations) and Biophysics (31 citations). Enoch Yeung has collaborated with scholars based in United States, United Kingdom and Luxembourg. Frequent co-authors include Richard M. Murray, Zachary Z. Sun, Vincent Noireaux, Clarmyra A. Hayes, Sebastian J. Maerkl, Henrike Niederholtmeyer, Yutaka Hori, Sean Warnick, Jorge Gonçalves and James L. Beck. Their work appears in journals such as Nature Communications, Journal of Computational Physics and eLife.
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.