Enoch Yeung

1.4k total citations
32 papers, 780 citations indexed

About

Enoch Yeung is a scholar working on Molecular Biology, Statistical and Nonlinear Physics and Computer Networks and Communications. According to data from OpenAlex, Enoch Yeung has authored 32 papers receiving a total of 780 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 10 papers in Statistical and Nonlinear Physics and 5 papers in Computer Networks and Communications. Recurrent topics in Enoch Yeung's work include Gene Regulatory Network Analysis (21 papers), Model Reduction and Neural Networks (7 papers) and Microbial Metabolic Engineering and Bioproduction (6 papers). Enoch Yeung is often cited by papers focused on Gene Regulatory Network Analysis (21 papers), Model Reduction and Neural Networks (7 papers) and Microbial Metabolic Engineering and Bioproduction (6 papers). Enoch Yeung collaborates with scholars based in United States, United Kingdom and Luxembourg. Enoch Yeung's co-authors include Richard M. Murray, Zachary Z. Sun, Clarmyra A. Hayes, Vincent Noireaux, Sebastian J. Maerkl, Yutaka Hori, Henrike Niederholtmeyer, Sean Warnick, Jorge Gonçalves and Andrew H. Ng and has published in prestigious journals such as Nature Communications, Journal of Computational Physics and eLife.

In The Last Decade

Enoch Yeung

31 papers receiving 770 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Enoch Yeung United States 11 615 138 95 75 66 32 780
Boyan Yordanov United Kingdom 15 727 1.2× 67 0.5× 126 1.3× 13 0.2× 137 2.1× 26 1.2k
Joerg Stelling Switzerland 16 915 1.5× 106 0.8× 171 1.8× 18 0.2× 27 0.4× 28 1.1k
Steffen Waldherr Germany 18 584 0.9× 77 0.6× 87 0.9× 34 0.5× 193 2.9× 85 900
Gabriele Lillacci United States 7 507 0.8× 104 0.8× 53 0.6× 25 0.3× 28 0.4× 13 627
William Ott United States 15 383 0.6× 121 0.9× 102 1.1× 165 2.2× 48 0.7× 40 737
Alan Veliz‐Cuba United States 14 535 0.9× 94 0.7× 42 0.4× 43 0.6× 24 0.4× 28 672
Nicole Radde Germany 14 502 0.8× 65 0.5× 26 0.3× 32 0.4× 35 0.5× 60 680
Vishwesh Kulkarni United States 14 320 0.5× 25 0.2× 57 0.6× 22 0.3× 232 3.5× 40 640
Madalena Chaves France 17 897 1.5× 161 1.2× 43 0.5× 68 0.9× 94 1.4× 63 1.1k
James Southern United Kingdom 11 280 0.5× 65 0.5× 103 1.1× 26 0.3× 8 0.1× 15 661

Countries citing papers authored by Enoch Yeung

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Clark, H. Brent, et al.. (2024). Modeling Control of Supercoiling Dynamics and Transcription Using DNA-Binding Proteins. IEEE Control Systems Letters. 8. 2253–2258. 1 indexed citations
2.
Haase, Steven B., et al.. (2023). Learning perturbation-inducible cell states from observability analysis of transcriptome dynamics. Nature Communications. 14(1). 3148–3148. 10 indexed citations
3.
Yeung, Enoch, Jongmin Kim, Ye Yuan, Jorge Gonçalves, & Richard M. Murray. (2021). Data-driven network models for genetic circuits from time-series data with incomplete measurements. Journal of The Royal Society Interface. 18(182). 20210413–20210413. 4 indexed citations
4.
Yeung, Enoch, et al.. (2021). Stability Analysis of Parameter Varying Genetic Toggle Switches Using Koopman Operators. Mathematics. 9(23). 3133–3133. 6 indexed citations
5.
Kim, Jongmin, Jongmin Kim, Jeongwon Kim, et al.. (2021). Synthetic logic circuits using RNA aptamer against T7 RNA polymerase. Biotechnology Journal. 17(3). e2000449–e2000449. 5 indexed citations
6.
Khan, Nymul E., et al.. (2020). A broad-host-range event detector: expanding and quantifying performance between Escherichia coli and Pseudomonas species. Munin Open Research Archive (The Arctic University of Norway). 5(1). 10 indexed citations
7.
Dorfan, Yuval, et al.. (2019). A data-driven method for quantifying the impact of a genetic circuit on its host. DSpace@MIT (Massachusetts Institute of Technology). 8 indexed citations
8.
Tschirhart, Tanya, Erin E. Kelly, Zachary Schultzhaus, et al.. (2019). Synthetic Biology Tools for the Fast-Growing Marine Bacterium Vibrio natriegens. ACS Synthetic Biology. 8(9). 2069–2079. 71 indexed citations
9.
You, Pengcheng, John Z. F. Pang, & Enoch Yeung. (2018). Stabilization of Power Networks via Market Dynamics. 139–145.
10.
You, Pengcheng, John Z. F. Pang, & Enoch Yeung. (2018). Deep Koopman Controller Synthesis for Cyber-Resilient Market-Based Frequency Regulation. IFAC-PapersOnLine. 51(28). 720–725. 12 indexed citations
11.
Kundu, Soumya, et al.. (2018). Decomposition of Nonlinear Dynamical Systems Using Koopman Gramians. 4811–4818. 6 indexed citations
12.
Kundu, Soumya, et al.. (2018). Decomposition of Nonlinear Dynamical Networks Via Comparison Systems. 190–196. 2 indexed citations
13.
Yeung, Enoch, Andrew H. Ng, Domitilla Del Vecchio, et al.. (2017). Biophysical Constraints Arising from Compositional Context in Synthetic Gene Networks. Cell Systems. 5(1). 11–24.e12. 86 indexed citations
14.
Niederholtmeyer, Henrike, Zachary Z. Sun, Yutaka Hori, et al.. (2015). Rapid cell-free forward engineering of novel genetic ring oscillators. eLife. 4. e09771–e09771. 173 indexed citations
15.
Yuan, Ye, Anurag Rai, Enoch Yeung, et al.. (2015). A Minimal Realization Technique for the Dynamical Structure Function of a Class of LTI Systems. IEEE Transactions on Control of Network Systems. 4(2). 301–311. 9 indexed citations
16.
Yeung, Enoch, Jongmin Kim, & Richard M. Murray. (2013). Resource competition as a source of non-minimum phase behavior in transcription-translation systems. Zenodo (CERN European Organization for Nuclear Research). 4060–4067. 9 indexed citations
17.
Yeung, Enoch, Ye Yuan, Jorge Gonçalves, et al.. (2012). Dynamical structure function identifiability conditions enabling signal structure reconstruction. Open Repository and Bibliography (University of Luxembourg). 4635–4641. 32 indexed citations
18.
Yeung, Enoch, Jongmin Kim, Ye Yuan, Jorge Gonçalves, & Richard M. Murray. (2012). Quantifying crosstalk in biochemical systems. Open Repository and Bibliography (University of Luxembourg). 10 indexed citations
19.
Xue, Mengran, Enoch Yeung, Anurag Rai, et al.. (2012). Initial-Condition Estimation in Network Synchronization Processes: Algebraic and Graphical Characterizations of the Estimator. Complex Systems. 21(4). 297–333. 12 indexed citations
20.
Yeung, Enoch, et al.. (2009). A Comparison of Network Reconstruction Methods for Chemical Reaction Networks. Open Repository and Bibliography (University of Luxembourg). 4 indexed citations

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.

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