Kwonmoo Lee

662 total citations
33 papers, 434 citations indexed

About

Kwonmoo Lee is a scholar working on Molecular Biology, Biophysics and Cell Biology. According to data from OpenAlex, Kwonmoo Lee has authored 33 papers receiving a total of 434 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 11 papers in Biophysics and 8 papers in Cell Biology. Recurrent topics in Kwonmoo Lee's work include Cell Image Analysis Techniques (10 papers), Cellular Mechanics and Interactions (6 papers) and Cell Adhesion Molecules Research (5 papers). Kwonmoo Lee is often cited by papers focused on Cell Image Analysis Techniques (10 papers), Cellular Mechanics and Interactions (6 papers) and Cell Adhesion Molecules Research (5 papers). Kwonmoo Lee collaborates with scholars based in United States, South Korea and United Kingdom. Kwonmoo Lee's co-authors include Jennifer L. Gallop, Marc W. Kirschner, Chuangqi Wang, Hee June Choi, Yongho Bae, Wokyung Sung, Ja Kang Ku, Hunter Elliott, Alex Groisman and Jessica Tytell and has published in prestigious journals such as Science, Nature Communications and Scientific Reports.

In The Last Decade

Kwonmoo Lee

32 papers receiving 427 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kwonmoo Lee United States 11 163 145 101 92 62 33 434
Sylvain Berlemont France 7 177 1.1× 88 0.6× 79 0.8× 60 0.7× 52 0.8× 16 383
Philippe Van Ham Belgium 16 145 0.9× 228 1.6× 183 1.8× 138 1.5× 145 2.3× 30 795
Anish R. Roy United States 10 63 0.4× 145 1.0× 124 1.2× 53 0.6× 57 0.9× 16 329
Nao Nitta Japan 10 31 0.2× 141 1.0× 132 1.3× 65 0.7× 186 3.0× 18 638
Ivan Rey‐Suarez United States 10 39 0.2× 84 0.6× 235 2.3× 45 0.5× 139 2.2× 12 398
Denis Tsygankov United States 17 279 1.7× 410 2.8× 99 1.0× 40 0.4× 53 0.9× 41 701
Feimo Shen United States 9 126 0.8× 160 1.1× 105 1.0× 19 0.2× 48 0.8× 14 406
Jutta Bulkescher Germany 7 92 0.6× 287 2.0× 145 1.4× 9 0.1× 42 0.7× 7 418
Jason M. Byars United States 8 88 0.5× 166 1.1× 299 3.0× 61 0.7× 160 2.6× 10 507
Philippe Roudot United States 12 149 0.9× 186 1.3× 335 3.3× 86 0.9× 160 2.6× 18 546

Countries citing papers authored by Kwonmoo Lee

Since Specialization
Citations

This map shows the geographic impact of Kwonmoo Lee'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 Kwonmoo Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kwonmoo Lee more than expected).

Fields of papers citing papers by Kwonmoo Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kwonmoo Lee. 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 Kwonmoo Lee. The network helps show where Kwonmoo Lee may publish in the future.

Co-authorship network of co-authors of Kwonmoo Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Kwonmoo Lee. A scholar is included among the top collaborators of Kwonmoo Lee 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 Kwonmoo Lee. Kwonmoo Lee 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.
Obuchi, Wataru, Kevin Leandro, David Rufino‐Ramos, et al.. (2025). Engineering of CD63 Enables Selective Extracellular Vesicle Cargo Loading and Enhanced Payload Delivery. Journal of Extracellular Vesicles. 14(6). e70094–e70094. 5 indexed citations
2.
Clemente, Leonardo, et al.. (2025). Fine-grained forecasting of COVID-19 trends at the county level in the United States. npj Digital Medicine. 8(1). 204–204. 1 indexed citations
3.
Lee, Kwonmoo, et al.. (2025). Heterogeneity-preserving discriminative feature selection for disease-specific subtype discovery. Nature Communications. 16(1). 3593–3593. 2 indexed citations
4.
Busatto, Sara, Hyung Joon Kim, Michael N. Lombardo, et al.. (2025). Breast Cancer‐Derived Extracellular Vesicles Modulate the Cytoplasmic and Cytoskeletal Dynamics of Blood‐Brain Barrier Endothelial Cells. Journal of Extracellular Vesicles. 14(1). e70038–e70038. 4 indexed citations
5.
Kim, Seong‐Eun, et al.. (2024). Deep Learning-Based Automated Analysis of NK Cell Cytotoxicity in Single Cancer Cell Arrays. BioChip Journal. 18(3). 453–463. 7 indexed citations
6.
Wang, Chuangqi, et al.. (2024). Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning. Advanced Science. 11(41). e2403547–e2403547. 3 indexed citations
7.
Brazzo, Joseph A., Kerry E. Poppenberg, Vincent M. Tutino, et al.. (2023). Survivin as a mediator of stiffness-induced cell cycle progression and proliferation of vascular smooth muscle cells. APL Bioengineering. 7(4). 46108–46108. 5 indexed citations
8.
Brazzo, Joseph A., Rhonda Drewes, John Kolega, et al.. (2023). Survivin regulates intracellular stiffness and extracellular matrix production in vascular smooth muscle cells. APL Bioengineering. 7(4). 46104–46104. 6 indexed citations
9.
Wang, Chuangqi, Nataša Reljin, David D. McManus, et al.. (2023). Deep cross-modal feature learning applied to predict acutely decompensated heart failure using in-home collected electrocardiography and transthoracic bioimpedance. Artificial Intelligence in Medicine. 140. 102548–102548. 14 indexed citations
10.
Kim, Young H., et al.. (2023). Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier. Scientific Reports. 13(1). 13525–13525. 6 indexed citations
11.
Lee, Kwonmoo, et al.. (2022). Protocol for live cell image segmentation to profile cellular morphodynamics using MARS-Net. STAR Protocols. 3(3). 101469–101469. 2 indexed citations
12.
13.
Wang, Chuangqi, et al.. (2021). A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy. Cell Reports Methods. 1(7). 100105–100105. 24 indexed citations
14.
Chakraborty, Paramita, Bindhu Verghese, Christine E. Farrance, et al.. (2021). Increased prevalence of indoor Aspergillus and Penicillium species is associated with indoor flooding and coastal proximity: a case study of 28 moldy buildings. Environmental Science Processes & Impacts. 23(11). 1681–1687. 10 indexed citations
15.
Wang, Chuangqi, Hee June Choi, Sung‐Jin Kim, et al.. (2018). Deconvolution of subcellular protrusion heterogeneity and the underlying actin regulator dynamics from live cell imaging. Nature Communications. 9(1). 1688–1688. 19 indexed citations
16.
17.
Lee, Kwonmoo, et al.. (2010). Self-Assembly of Filopodia-Like Structures on Supported Lipid Bilayers. Science. 329(5997). 1341–1345. 130 indexed citations
18.
Lee, Kwonmoo & W. Sung. (1999). Effects of nonequilibrium fluctuations on ionic transport through biomembranes. Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics. 60(4). 4681–4686. 10 indexed citations
19.
Lee, Kwonmoo, et al.. (1995). Direct formation of a state-selected excited state of Fe atoms by multiphoton dissociation of Fe(CO)5 at atomic transition frequencies. Chemical Physics Letters. 244(3-4). 213–217. 11 indexed citations
20.
Lee, Kwonmoo, et al.. (1993). Radiative lifetimes and intramultiplet mixing rate constants for Ga(4d) atoms in Ar. Chemical Physics Letters. 216(3-6). 483–487. 6 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Sylvain Berlemont Breakdown of academic impact, for papers by Philippe Van Ham Breakdown of academic impact, for papers by Anish R. Roy Breakdown of academic impact, for papers by Nao Nitta Breakdown of academic impact, for papers by Ivan Rey‐Suarez Breakdown of academic impact, for papers by Denis Tsygankov Breakdown of academic impact, for papers by Feimo Shen Breakdown of academic impact, for papers by Jutta Bulkescher Breakdown of academic impact, for papers by Jason M. Byars Breakdown of academic impact, for papers by Philippe Roudot
Rankless by CCL
2026