Yoontae Lee

11.4k total citations · 4 hit papers
38 papers, 8.3k citations indexed

About

Yoontae Lee is a scholar working on Molecular Biology, Immunology and Cancer Research. According to data from OpenAlex, Yoontae Lee has authored 38 papers receiving a total of 8.3k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Molecular Biology, 10 papers in Immunology and 9 papers in Cancer Research. Recurrent topics in Yoontae Lee's work include T-cell and B-cell Immunology (6 papers), MicroRNA in disease regulation (5 papers) and Immune Cell Function and Interaction (5 papers). Yoontae Lee is often cited by papers focused on T-cell and B-cell Immunology (6 papers), MicroRNA in disease regulation (5 papers) and Immune Cell Function and Interaction (5 papers). Yoontae Lee collaborates with scholars based in South Korea, United States and Canada. Yoontae Lee's co-authors include Jinju Han, V. Narry Kim, Minju Kim, Sanghyuk Lee, Sung Hee Baek, Young Kook Kim, Hua Jin, Je‐Keun Rhee, Byoung-Tak Zhang and Jin‐Wu Nam and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Yoontae Lee

38 papers receiving 8.1k citations

Hit Papers

MicroRNA genes are transcribed by RNA polymerase II 2004 2026 2011 2018 2004 2004 2006 2006 1000 2.0k 3.0k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yoontae Lee South Korea 22 6.9k 5.3k 1.0k 612 356 38 8.3k
Marc R. Fabian Canada 31 5.8k 0.9× 3.3k 0.6× 714 0.7× 550 0.9× 273 0.8× 58 7.4k
Chanseok Shin South Korea 27 5.9k 0.9× 4.2k 0.8× 784 0.8× 521 0.9× 296 0.8× 61 7.3k
Philip W. Garrett-Engele United States 14 8.6k 1.2× 6.0k 1.1× 450 0.4× 578 0.9× 430 1.2× 15 10.0k
Mariana Lagos‐Quintana United States 11 8.5k 1.2× 6.8k 1.3× 1.6k 1.6× 555 0.9× 580 1.6× 14 10.5k
Harpreet K. Saini United Kingdom 22 4.5k 0.7× 3.5k 0.7× 850 0.8× 343 0.6× 308 0.9× 47 5.9k
Minju Ha South Korea 13 6.4k 0.9× 4.7k 0.9× 334 0.3× 475 0.8× 239 0.7× 16 7.6k
Jinju Han South Korea 19 12.9k 1.9× 10.6k 2.0× 1.3k 1.3× 837 1.4× 706 2.0× 29 15.2k
Janell M. Schelter United States 17 8.0k 1.2× 5.5k 1.0× 410 0.4× 652 1.1× 670 1.9× 20 9.5k
Mo‐Fang Liu China 36 5.4k 0.8× 3.4k 0.6× 830 0.8× 539 0.9× 562 1.6× 73 6.6k
Neil Cooch United States 14 6.0k 0.9× 3.6k 0.7× 351 0.3× 474 0.8× 547 1.5× 25 6.8k

Countries citing papers authored by Yoontae Lee

Since Specialization
Citations

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

Fields of papers citing papers by Yoontae Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yoontae Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Yoontae Lee. A scholar is included among the top collaborators of Yoontae 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 Yoontae Lee. Yoontae 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.
Lee, Yoontae, et al.. (2025). The role of transcription factors in prostate cancer progression. Molecules and Cells. 48(4). 100193–100193. 1 indexed citations
2.
Kim, Dasom, et al.. (2024). Cytosolic N-terminal formyl-methionine deformylation derives cancer stem cell features and tumor progression. Scientific Reports. 14(1). 14900–14900. 2 indexed citations
3.
Park, Ji‐Ho, Dasom Kim, June‐Yong Lee, et al.. (2024). ETV5 promotes lupus pathogenesis and follicular helper T cell differentiation by inducing osteopontin expression. Proceedings of the National Academy of Sciences. 121(24). e2322009121–e2322009121. 4 indexed citations
4.
Golkaram, Mahdi, et al.. (2024). ETV4 is a mechanical transducer linking cell crowding dynamics to lineage specification. Nature Cell Biology. 26(6). 903–916. 9 indexed citations
5.
Kim, Tae-Kyung, et al.. (2024). The capicua-ataxin-1-like complex regulates Notch-driven marginal zone B cell development and sepsis progression. Nature Communications. 15(1). 10579–10579. 1 indexed citations
6.
Kim, Chan Johng, Jinyong Choi, Yoon Kyung Jeon, et al.. (2023). The transcription factor Mef2d regulates B:T synapse–dependent GC-T FH differentiation and IL-21–mediated humoral immunity. Science Immunology. 8(81). eadf2248–eadf2248. 13 indexed citations
7.
Lee, Yoontae, et al.. (2023). The ubiquitin–proteasome system links NADPH metabolism to ferroptosis. Trends in Cell Biology. 33(12). 1088–1103. 33 indexed citations
8.
Nguyen, Kha The, Eunjeong Kim, Dasom Kim, et al.. (2022). The MARCHF6 E3 ubiquitin ligase acts as an NADPH sensor for the regulation of ferroptosis. Nature Cell Biology. 24(8). 1239–1251. 56 indexed citations
9.
Park, Joonyoung, et al.. (2022). ERK phosphorylation disrupts the intramolecular interaction of capicua to promote cytoplasmic translocation of capicua and tumor growth. Frontiers in Molecular Biosciences. 9. 1030725–1030725. 3 indexed citations
10.
Park, Ji‐Ho, Su Jin Lee, Tae Jin Kim, et al.. (2022). Postnatal regulation of B-1a cell development and survival by the CIC-PER2-BHLHE41 axis. Cell Reports. 38(7). 110386–110386. 10 indexed citations
11.
12.
Kim, Eunjeong, Donghyo Kim, Chang‐Jin Kim, et al.. (2020). Capicua suppresses colorectal cancer progression via repression of ETV4 expression. Cancer Cell International. 20(1). 42–42. 19 indexed citations
13.
Lee, Kihwan, Hyun-Ju Kim, Kyongman An, et al.. (2016). Replenishment of microRNA-188-5p restores the synaptic and cognitive deficits in 5XFAD Mouse Model of Alzheimer’s Disease. Scientific Reports. 6(1). 34433–34433. 62 indexed citations
14.
Kim, Eunjeong, Sung-Jun Park, Nahyun Choi, et al.. (2015). Deficiency of Capicua disrupts bile acid homeostasis. Scientific Reports. 5(1). 8272–8272. 26 indexed citations
15.
Han, Kihoon, Vincenzo A. Gennarino, Yoontae Lee, et al.. (2013). Human-specific regulation of MeCP2 levels in fetal brains by microRNA miR-483-5p. Genes & Development. 27(5). 485–490. 86 indexed citations
16.
Kahle, Juliette J., George P. Souroullas, Peng Yu, et al.. (2013). Ataxin1L Is a Regulator of HSC Function Highlighting the Utility of Cross-Tissue Comparisons for Gene Discovery. PLoS Genetics. 9(3). e1003359–e1003359. 6 indexed citations
17.
Lee, Yoontae, John Denis Fryer, Hyojin Kang, et al.. (2011). ATXN1 Protein Family and CIC Regulate Extracellular Matrix Remodeling and Lung Alveolarization. Developmental Cell. 21(4). 746–757. 82 indexed citations
18.
Lee, Yoontae, Rodney C. Samaco, Jennifer R. Gatchel, et al.. (2008). miR-19, miR-101 and miR-130 co-regulate ATXN1 levels to potentially modulate SCA1 pathogenesis. Nature Neuroscience. 11(10). 1137–1139. 179 indexed citations
19.
Lee, Yoontae, Minju Kim, Jinju Han, et al.. (2004). MicroRNA genes are transcribed by RNA polymerase II. The EMBO Journal. 23(20). 4051–4060. 3254 indexed citations breakdown →
20.
Han, Jinju, et al.. (2004). The Drosha-DGCR8 complex in primary microRNA processing. Genes & Development. 18(24). 3016–3027. 1645 indexed citations breakdown →

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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