Hyunji Lee

2.1k total citations · 1 hit paper
56 papers, 1.5k citations indexed

About

Hyunji Lee is a scholar working on Molecular Biology, Immunology and Oncology. According to data from OpenAlex, Hyunji Lee has authored 56 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Molecular Biology, 13 papers in Immunology and 10 papers in Oncology. Recurrent topics in Hyunji Lee's work include CRISPR and Genetic Engineering (12 papers), T-cell and B-cell Immunology (12 papers) and Immune Cell Function and Interaction (11 papers). Hyunji Lee is often cited by papers focused on CRISPR and Genetic Engineering (12 papers), T-cell and B-cell Immunology (12 papers) and Immune Cell Function and Interaction (11 papers). Hyunji Lee collaborates with scholars based in South Korea, United States and Puerto Rico. Hyunji Lee's co-authors include Jin‐Soo Kim, Gayoung Baek, Kyoungmi Kim, Seonghyun Lee, Se‐Ho Park, Taeyoung Koo, Da-eun Kim, Heon Seok Kim, Kayeong Lim and Sang‐Tae Kim and has published in prestigious journals such as Nature Communications, The Journal of Experimental Medicine and SHILAP Revista de lepidopterología.

In The Last Decade

Hyunji Lee

53 papers receiving 1.4k citations

Hit Papers

Adenine base editing in m... 2018 2026 2020 2023 2018 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hyunji Lee South Korea 18 990 310 262 167 90 56 1.5k
Yuanwu Ma China 23 1.3k 1.3× 686 2.2× 153 0.6× 144 0.9× 45 0.5× 47 2.0k
Séverine Ménoret France 24 1.1k 1.1× 503 1.6× 625 2.4× 131 0.8× 68 0.8× 54 2.0k
S. Kaye Spratt United States 18 1.6k 1.7× 144 0.5× 623 2.4× 270 1.6× 79 0.9× 24 2.2k
Yuichiro Miyaoka Japan 14 1.0k 1.1× 98 0.3× 276 1.1× 281 1.7× 83 0.9× 22 1.8k
Takako Usami Japan 9 1.3k 1.3× 158 0.5× 344 1.3× 410 2.5× 125 1.4× 11 1.5k
Klaudia Kuranda France 16 1.1k 1.1× 251 0.8× 743 2.8× 504 3.0× 61 0.7× 24 1.7k
Chun‐Qing Song China 18 1.2k 1.2× 53 0.2× 295 1.1× 140 0.8× 65 0.7× 27 1.3k
Boris Kantor United States 21 1.1k 1.1× 114 0.4× 671 2.6× 104 0.6× 16 0.2× 44 1.5k
Rebecca H. Herbst United States 9 1.4k 1.4× 417 1.3× 173 0.7× 275 1.6× 106 1.2× 11 1.9k
Thomas G. Fazzio United States 25 2.6k 2.6× 155 0.5× 315 1.2× 163 1.0× 262 2.9× 43 3.0k

Countries citing papers authored by Hyunji Lee

Since Specialization
Citations

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

Fields of papers citing papers by Hyunji Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hyunji Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Hyunji Lee. A scholar is included among the top collaborators of Hyunji 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 Hyunji Lee. Hyunji 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.
Yang, Jiyun, Sung Han Ok, Hyunji Lee, et al.. (2025). An innovative approach using CRISPR-ribonucleoprotein packaged in virus-like particles to generate genetically engineered mouse models. Nature Communications. 16(1). 3451–3451.
2.
Choe, Yeong Sim, Regina E. Y. Kim, Jee Young Kim, et al.. (2024). Automated Scoring of Alzheimer’s Disease Atrophy Scale with Subtype Classification Using Deep Learning-Based T1-Weighted Magnetic Resonance Image Segmentation. Journal of Alzheimer s Disease Reports. 8(1). 863–876.
3.
Kim, Kyoungmi, et al.. (2023). Clinical Approaches for Mitochondrial Diseases. Cells. 12(20). 2494–2494. 9 indexed citations
4.
Lee, Hyunji, et al.. (2023). Recent Research Trends in Stem Cells Using CRISPR/Cas-Based Genome Editing Methods. International Journal of Stem Cells. 17(1). 1–14. 3 indexed citations
5.
Kim, Narae, et al.. (2023). Precise base editing without unintended indels in human cells and mouse primary myoblasts. Experimental & Molecular Medicine. 55(12). 2586–2595. 5 indexed citations
6.
Lee, Hyunji, Jun Kim, & Junho Lee. (2023). Benchmarking datasets for assembly-based variant calling using high-fidelity long reads. BMC Genomics. 24(1). 148–148. 16 indexed citations
7.
Lee, Hyunji, et al.. (2023). Trends and prospects in mitochondrial genome editing. Experimental & Molecular Medicine. 55(5). 871–878. 14 indexed citations
8.
Choi, Jungmin, et al.. (2021). Targeted mutagenesis in mouse cells and embryos using an enhanced prime editor. Genome biology. 22(1). 170–170. 94 indexed citations
9.
Lee, Hyunji, et al.. (2020). Genome editing methods in animal models. Animal Cells and Systems. 24(1). 8–16. 37 indexed citations
10.
Kim, Yu‐Hee, Kyung‐Ah Cho, Hyunji Lee, et al.. (2019). Identification of WNT16 as a Predictable Biomarker for Accelerated Osteogenic Differentiation of Tonsil-Derived Mesenchymal Stem Cells In Vitro. Stem Cells International. 2019. 1–10. 11 indexed citations
11.
Lee, Doh Young, Hyunji Lee, Byoungjae Kim, et al.. (2015). Office-Based Laser Surgery for Benign Laryngeal Lesion. Medical Lasers. 4(2). 65–69. 4 indexed citations
12.
Shin, Jung Hoon, et al.. (2012). Mutation of a Positively Charged Cytoplasmic Motif within CD1d Results in Multiple Defects in Antigen Presentation to NKT Cells. The Journal of Immunology. 188(5). 2235–2243. 6 indexed citations
13.
Kim, Kyoungmi, Hyunji Lee, David W. Threadgill, & Daekee Lee. (2011). Epiregulin-dependent amphiregulin expression and ERBB2 signaling are involved in luteinizing hormone-induced paracrine signaling pathways in mouse ovary. Biochemical and Biophysical Research Communications. 405(2). 319–324. 22 indexed citations
14.
Lee, Hyunji, et al.. (2011). An Impact of the Parental Burden on Quality of Life of Disabled Adolescents' Parents : The Mediating Effect of Family Communication and Family Functioning. 15(3). 131–153. 2 indexed citations
15.
Hong, Changwan, et al.. (2010). NKT Cell-Dependent Regulation of Secondary Antigen-Specific, Conventional CD4+ T Cell Immune Responses. The Journal of Immunology. 184(10). 5589–5594. 15 indexed citations
16.
Lee, Hyunji, Ji Hyun Kim, Sung Yeun Yang, et al.. (2010). Peripheral blood gene expression of B7 and CD28 family members associated with tumor progression and microscopic lymphovascular invasion in colon cancer patients. Journal of Cancer Research and Clinical Oncology. 136(9). 1445–1452. 39 indexed citations
17.
Hong, Changwan, et al.. (2009). Regulation of Secondary Antigen-Specific CD8+ T-Cell Responses by Natural Killer T Cells. Cancer Research. 69(10). 4301–4308. 17 indexed citations
18.
Hong, Changwan, et al.. (2009). A New Reporter Vector System Based on Flow-Cytometry to Detect Promoter Activity. Immune Network. 9(6). 243–243. 4 indexed citations
19.
Hong, Changwan, Hyunji Lee, Jung Hoon Shin, et al.. (2009). Natural killer T cells promote collagen-induced arthritis in DBA/1 mice. Biochemical and Biophysical Research Communications. 390(3). 399–403. 14 indexed citations
20.
Chung, Kyung‐Sook, Ji‐Youn Kim, Hyunji Lee, et al.. (2007). Rapid Screen of Human Genes for Relevance to Cancer Using Fission Yeast. SLAS DISCOVERY. 12(4). 568–577. 13 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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