Ping‐Hsien Lee

1.3k total citations · 1 hit paper
16 papers, 911 citations indexed

About

Ping‐Hsien Lee is a scholar working on Immunology, Molecular Biology and Oncology. According to data from OpenAlex, Ping‐Hsien Lee has authored 16 papers receiving a total of 911 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Immunology, 6 papers in Molecular Biology and 6 papers in Oncology. Recurrent topics in Ping‐Hsien Lee's work include Immune Cell Function and Interaction (8 papers), CAR-T cell therapy research (6 papers) and Cytomegalovirus and herpesvirus research (3 papers). Ping‐Hsien Lee is often cited by papers focused on Immune Cell Function and Interaction (8 papers), CAR-T cell therapy research (6 papers) and Cytomegalovirus and herpesvirus research (3 papers). Ping‐Hsien Lee collaborates with scholars based in United States, United Kingdom and Japan. Ping‐Hsien Lee's co-authors include Nicholas P. Restifo, Tori N. Yamamoto, Suman K. Vodnala, Zhiya Yu, Rigel J. Kishton, Christopher A. Klebanoff, Robert Eil, Madhusudhanan Sukumar, Toren Finkel and Rahul Roychoudhuri and has published in prestigious journals such as Science, Journal of Clinical Investigation and The Journal of Experimental Medicine.

In The Last Decade

Ping‐Hsien Lee

15 papers receiving 897 citations

Hit Papers

T cell stemness and dysfunction in tumors are triggered b... 2019 2026 2021 2023 2019 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ping‐Hsien Lee United States 11 441 349 332 164 107 16 911
Geoffrey Guittard France 14 540 1.2× 419 1.2× 476 1.4× 70 0.4× 76 0.7× 27 1.1k
Edahí González‐Avalos United States 12 696 1.6× 696 2.0× 545 1.6× 137 0.8× 86 0.8× 14 1.4k
ZeNan Chang United States 12 177 0.4× 297 0.9× 407 1.2× 99 0.6× 161 1.5× 15 729
H. Carlo Maurer United States 17 201 0.5× 493 1.4× 370 1.1× 157 1.0× 95 0.9× 33 931
Yuanming Xu United States 15 350 0.8× 353 1.0× 257 0.8× 51 0.3× 46 0.4× 17 918
Inmoo Rhee South Korea 16 735 1.7× 577 1.7× 285 0.9× 78 0.5× 52 0.5× 30 1.2k
Lilach Abramovitz Israel 11 245 0.6× 452 1.3× 376 1.1× 134 0.8× 94 0.9× 13 967
Stacy A. Decker United States 9 377 0.9× 191 0.5× 293 0.9× 50 0.3× 55 0.5× 10 766
Dorota D. Klysz United States 5 381 0.9× 302 0.9× 359 1.1× 111 0.7× 179 1.7× 5 831
Megan M. Wyatt United States 14 429 1.0× 190 0.5× 537 1.6× 83 0.5× 103 1.0× 30 773

Countries citing papers authored by Ping‐Hsien Lee

Since Specialization
Citations

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

Fields of papers citing papers by Ping‐Hsien Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ping‐Hsien Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Ping‐Hsien Lee. A scholar is included among the top collaborators of Ping‐Hsien 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 Ping‐Hsien Lee. Ping‐Hsien Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
1.
Merlino, Ashley A., et al.. (2024). Abstract 52: CRISPR-mediated knock-out of A20 lowers the activation threshold and induces metabolic rewiring of CAR-T cells. Cancer Research. 84(6_Supplement). 52–52.
2.
Stanojević, Maja, Melanie Grant, Ping‐Hsien Lee, et al.. (2021). Identification of novel HLA-restricted preferentially expressed antigen in melanoma peptides to facilitate off-the-shelf tumor-associated antigen-specific T-cell therapies. Cytotherapy. 23(8). 694–703. 10 indexed citations
3.
Lee, Ping‐Hsien, Michael D. Keller, Patrick J. Hanley, & Catherine M. Bollard. (2020). Virus-Specific T Cell Therapies for HIV: Lessons Learned From Hematopoietic Stem Cell Transplantation. Frontiers in Cellular and Infection Microbiology. 10. 298–298. 8 indexed citations
4.
Kanellopoulou, Chryssa, Alex George, Evan Masutani, et al.. (2019). Mg2+ regulation of kinase signaling and immune function. The Journal of Experimental Medicine. 216(8). 1828–1842. 57 indexed citations
5.
Lee, Ping‐Hsien, Tori N. Yamamoto, Devikala Gurusamy, et al.. (2019). Host conditioning with IL-1β improves the antitumor function of adoptively transferred T cells. The Journal of Experimental Medicine. 216(11). 2619–2634. 60 indexed citations
6.
Yamamoto, Tori N., Ping‐Hsien Lee, Suman K. Vodnala, et al.. (2019). T cells genetically engineered to overcome death signaling enhance adoptive cancer immunotherapy. Journal of Clinical Investigation. 129(4). 1551–1565. 110 indexed citations
7.
Vodnala, Suman K., Robert Eil, Rigel J. Kishton, et al.. (2019). T cell stemness and dysfunction in tumors are triggered by a common mechanism. Science. 363(6434). 409 indexed citations breakdown →
8.
Kurashige, Mahiro, Yi Liu, Yu Ishimoto, et al.. (2018). A cleavage product of Polycystin-1 is a mitochondrial matrix protein that affects mitochondria morphology and function when heterologously expressed. Scientific Reports. 8(1). 2743–2743. 78 indexed citations
9.
Kawabe, Takeshi, Dragana Janković, Yuefeng Huang, et al.. (2017). Memory-phenotype CD4 + T cells spontaneously generated under steady-state conditions exert innate T H 1-like effector function. Science Immunology. 2(12). 64 indexed citations
10.
Kawabe, Takeshi, Dragana Janković, Yuefeng Huang, et al.. (2017). Memory-phenotype CD4+ T cells spontaneously generated under steady state conditions exert innate Th1-like effector function. The Journal of Immunology. 198(Supplement_1). 150.4–150.4. 7 indexed citations
11.
Lee, Ping‐Hsien, et al.. (2015). G0S2 modulates homeostatic proliferation of naïve CD8+ T cells and inhibits oxidative phosphorylation in mitochondria. Immunology and Cell Biology. 93(7). 605–615. 11 indexed citations
12.
Mamonkin, Maksim, et al.. (2013). Differential roles of KLF4 in the development and differentiation of CD8+ T cells. Immunology Letters. 156(1-2). 94–101. 18 indexed citations
13.
Lee, Ping‐Hsien, et al.. (2013). The Transcription Factor E74-like Factor 4 Suppresses Differentiation of Proliferating CD4+ T Cells to the Th17 Lineage. The Journal of Immunology. 192(1). 178–188. 26 indexed citations
14.
Park, Chun Shik, et al.. (2012). Krüppel-like factor 4 (KLF4) promotes the survival of natural killer cells and maintains the number of conventional dendritic cells in the spleen. Journal of Leukocyte Biology. 91(5). 739–750. 31 indexed citations
15.
Yamada, Takeshi, et al.. (2011). G0S2, an early response gene, regulates quiescence in naive T cells (104.10). The Journal of Immunology. 186(1_Supplement). 104.10–104.10. 2 indexed citations
16.
Yamada, Takeshi, et al.. (2010). Cutting Edge: Expression of the Transcription Factor E74-Like Factor 4 Is Regulated by the Mammalian Target of Rapamycin Pathway in CD8+ T Cells. The Journal of Immunology. 185(7). 3824–3828. 20 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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