Hyeong-Min Lee

2.0k total citations
23 papers, 1.3k citations indexed

About

Hyeong-Min Lee is a scholar working on Molecular Biology, Endocrine and Autonomic Systems and Genetics. According to data from OpenAlex, Hyeong-Min Lee has authored 23 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 5 papers in Endocrine and Autonomic Systems and 5 papers in Genetics. Recurrent topics in Hyeong-Min Lee's work include Circadian rhythm and melatonin (5 papers), Genetic Syndromes and Imprinting (4 papers) and Receptor Mechanisms and Signaling (4 papers). Hyeong-Min Lee is often cited by papers focused on Circadian rhythm and melatonin (5 papers), Genetic Syndromes and Imprinting (4 papers) and Receptor Mechanisms and Signaling (4 papers). Hyeong-Min Lee collaborates with scholars based in United States, South Korea and United Kingdom. Hyeong-Min Lee's co-authors include Rongmin Chen, Choogon Lee, Yongjin Lee, Bryan L. Roth, Yuna Kim, Noah Sciaky, Benjamin D. Philpot, Seung‐Hee Yoo, Jian Jin and Philippe Giguère and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Hyeong-Min Lee

20 papers receiving 1.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hyeong-Min Lee United States 15 596 527 362 281 241 23 1.3k
Krista Kaasik United States 13 685 1.1× 1.2k 2.3× 197 0.5× 298 1.1× 364 1.5× 14 2.1k
A. Lamouroux France 21 1.1k 1.8× 412 0.8× 310 0.9× 937 3.3× 270 1.1× 27 2.3k
Katja Vanselow Germany 7 386 0.6× 695 1.3× 123 0.3× 160 0.6× 340 1.4× 8 1.1k
Shahaf Peleg Germany 11 839 1.4× 137 0.3× 241 0.7× 163 0.6× 46 0.2× 19 1.3k
María S. Robles Germany 14 572 1.0× 1.1k 2.2× 55 0.2× 302 1.1× 410 1.7× 23 1.9k
Aline Gréchez‐Cassiau France 14 289 0.5× 973 1.8× 65 0.2× 161 0.6× 228 0.9× 22 1.3k
Filippo Tamanini Netherlands 25 1.2k 2.0× 1.5k 2.8× 818 2.3× 532 1.9× 811 3.4× 38 2.9k
Yongli Shan United States 12 300 0.5× 624 1.2× 57 0.2× 219 0.8× 283 1.2× 15 987
Pascal Gos Switzerland 14 499 0.8× 839 1.6× 46 0.1× 207 0.7× 209 0.9× 14 1.5k
Shigeru Mitsui Japan 6 345 0.6× 1.3k 2.5× 59 0.2× 307 1.1× 459 1.9× 7 1.7k

Countries citing papers authored by Hyeong-Min Lee

Since Specialization
Citations

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

Fields of papers citing papers by Hyeong-Min Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hyeong-Min Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Hyeong-Min Lee. A scholar is included among the top collaborators of Hyeong-Min 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 Hyeong-Min Lee. Hyeong-Min 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.
Bettadapur, Kiran R., Justin Cotney, Jon L. Collins, et al.. (2024). Ube3a unsilencer for the potential treatment of Angelman syndrome. Nature Communications. 15(1). 5558–5558. 9 indexed citations
2.
Kim, Yuna & Hyeong-Min Lee. (2024). Acidic solvent improves cisplatin action in in-vitro. Biochemical and Biophysical Research Communications. 712-713. 149936–149936. 2 indexed citations
3.
4.
Zubair, Asif, Richard H. Chapple, Sivaraman Natarajan, et al.. (2022). Cell type identification in spatial transcriptomics data can be improved by leveraging cell-type-informative paired tissue images using a Bayesian probabilistic model. Nucleic Acids Research. 50(14). e80–e80. 9 indexed citations
5.
Zubair, Asif, Sivaraman Natarajan, William C. Wright, et al.. (2022). Abstract 456: Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors. Cancer Research. 82(12_Supplement). 456–456. 1 indexed citations
6.
Lee, Hyeong-Min, et al.. (2021). Efficient brazzein production in yeast (Kluyveromyces lactis) using a chemically defined medium. Bioprocess and Biosystems Engineering. 44(4). 913–925. 9 indexed citations
7.
Zhang, Hao, Yang Zhang, Xinyue Zhou, et al.. (2020). Functional interrogation of HOXA9 regulome in MLLr leukemia via reporter-based CRISPR/Cas9 screen. eLife. 9. 27 indexed citations
8.
Bridges, Arlene S., Jayalakshmi Miriyala, Hsien‐Sung Huang, et al.. (2020). Topoisomerase inhibitors unsilence the dormant allele of Ube3a in neurons. UNC Libraries.
9.
Lee, Hyeong-Min, Megumi Aita, Noah Sciaky, et al.. (2020). A small-molecule screen reveals novel modulators of MeCP2 and X-chromosome inactivation maintenance. Journal of Neurodevelopmental Disorders. 12(1). 29–29. 20 indexed citations
10.
11.
Lee, Hyeong-Min, et al.. (2017). A Case of Cardiorenal Syndrome Treated with Korean Medicine. The Journal of Internal Korean Medicine. 38(5). 610–618. 1 indexed citations
12.
Kim, Yuna, Hyeong-Min Lee, Yan Xiong, et al.. (2016). Targeting the histone methyltransferase G9a activates imprinted genes and improves survival of a mouse model of Prader–Willi syndrome. Nature Medicine. 23(2). 213–222. 93 indexed citations
13.
Mabb, Angela M., Jeremy M. Simon, Ian King, et al.. (2016). Topoisomerase 1 Regulates Gene Expression in Neurons through Cleavage Complex-Dependent and -Independent Mechanisms. PLoS ONE. 11(5). e0156439–e0156439. 36 indexed citations
14.
Lee, Hyeong-Min & Yuna Kim. (2016). Drug Repurposing Is a New Opportunity for Developing Drugs against Neuropsychiatric Disorders. SHILAP Revista de lepidopterología. 2016. 1–12. 62 indexed citations
15.
Lee, Hyeong-Min, Philippe Giguère, & Bryan L. Roth. (2013). DREADDs: novel tools for drug discovery and development. Drug Discovery Today. 19(4). 469–473. 74 indexed citations
16.
Farrell, Martilias S., Ying Pei, Yehong Wan, et al.. (2012). A Gαs DREADD Mouse for Selective Modulation of cAMP Production in Striatopallidal Neurons. Neuropsychopharmacology. 38(5). 854–862. 100 indexed citations
17.
Huang, Hsien‐Sung, John A. Allen, Angela M. Mabb, et al.. (2011). Topoisomerase inhibitors unsilence the dormant allele of Ube3a in neurons. Nature. 481(7380). 185–189. 287 indexed citations
18.
Lee, Yongjin, Rongmin Chen, Hyeong-Min Lee, & Choogon Lee. (2011). Stoichiometric Relationship among Clock Proteins Determines Robustness of Circadian Rhythms. Journal of Biological Chemistry. 286(9). 7033–7042. 66 indexed citations
19.
Chen, Rongmin, Aaron E. Schirmer, Yongjin Lee, et al.. (2009). Rhythmic PER Abundance Defines a Critical Nodal Point for Negative Feedback within the Circadian Clock Mechanism. Molecular Cell. 36(3). 417–430. 183 indexed citations
20.
Prosser, Rebecca A., et al.. (2006). Serotonergic pre-treatments block in vitro serotonergic phase shifts of the mouse suprachiasmatic nucleus circadian clock. Neuroscience. 142(2). 547–555. 31 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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