Jae K. Lee

5.3k total citations · 1 hit paper
62 papers, 3.7k citations indexed

About

Jae K. Lee is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Developmental Neuroscience. According to data from OpenAlex, Jae K. Lee has authored 62 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 25 papers in Cellular and Molecular Neuroscience, 21 papers in Molecular Biology and 21 papers in Developmental Neuroscience. Recurrent topics in Jae K. Lee's work include Nerve injury and regeneration (24 papers), Neurogenesis and neuroplasticity mechanisms (20 papers) and Spinal Cord Injury Research (16 papers). Jae K. Lee is often cited by papers focused on Nerve injury and regeneration (24 papers), Neurogenesis and neuroplasticity mechanisms (20 papers) and Spinal Cord Injury Research (16 papers). Jae K. Lee collaborates with scholars based in United States, South Korea and China. Jae K. Lee's co-authors include Binhai Zheng, Lindsay M. Milich, Kevin K. Park, Cynthia Soderblom, Pantelis Tsoulfas, Do-Hun Lee, Stephanie L. Yahn, Amber R. Hackett, Yunjiao Zhu and Rafer Willenberg and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Advanced Materials and Nature Communications.

In The Last Decade

Jae K. Lee

61 papers receiving 3.7k citations

Hit Papers

PTEN deletion enhances the regenerative ability of adult ... 2010 2026 2015 2020 2010 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jae K. Lee United States 28 2.0k 1.3k 1.2k 1.2k 672 62 3.7k
Timothy M. O’Shea United States 17 1.3k 0.6× 964 0.7× 803 0.7× 773 0.7× 815 1.2× 28 3.1k
Shuxin Li United States 25 2.5k 1.2× 1.2k 0.9× 1.3k 1.1× 1.1k 1.0× 475 0.7× 48 3.7k
Jason R. Plemel Canada 30 1.5k 0.8× 1.9k 1.4× 1.5k 1.3× 1.1k 1.0× 1.1k 1.6× 54 4.2k
Joshua E. Burda United States 15 1.7k 0.9× 1.1k 0.9× 1.2k 1.0× 1.3k 1.1× 1.6k 2.4× 17 4.4k
Pantelis Tsoulfas United States 38 2.6k 1.3× 790 0.6× 1.6k 1.4× 2.1k 1.8× 412 0.6× 62 4.7k
Soheila Karimi‐Abdolrezaee Canada 30 2.3k 1.1× 2.5k 1.9× 1.4k 1.2× 1.2k 1.1× 565 0.8× 52 4.7k
Kevin K. Park United States 22 3.2k 1.6× 784 0.6× 1.9k 1.6× 2.2k 1.9× 469 0.7× 38 4.8k
Sarah A. Busch United States 13 1.6k 0.8× 882 0.7× 835 0.7× 706 0.6× 344 0.5× 15 2.5k
Jared H. Miller United States 4 2.0k 1.0× 860 0.7× 1.2k 1.1× 740 0.6× 505 0.8× 5 2.9k
William B.J. Cafferty United States 29 2.2k 1.1× 701 0.5× 1.1k 0.9× 857 0.7× 365 0.5× 41 3.1k

Countries citing papers authored by Jae K. Lee

Since Specialization
Citations

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

Fields of papers citing papers by Jae K. Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jae K. Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Jae K. Lee. A scholar is included among the top collaborators of Jae K. 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 Jae K. Lee. Jae K. 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
2.
Kang, Brian Byunghyun, et al.. (2025). Molecular pathology of acute spinal cord injury in middle-aged mice. Journal of Neuroinflammation. 22(1). 181–181. 1 indexed citations
4.
Choi, James S., Roman Sankowski, Lukas Amann, et al.. (2024). Interaction between subventricular zone microglia and neural stem cells impacts the neurogenic response in a mouse model of cortical ischemic stroke. Nature Communications. 15(1). 9095–9095. 5 indexed citations
5.
Choi, James S., et al.. (2022). A systematic evaluation of the computational tools for ligand-receptor-based cell–cell interaction inference. Briefings in Functional Genomics. 21(5). 339–356. 18 indexed citations
6.
Cerqueira, Susana R., et al.. (2022). BET protein inhibition promotes non-myeloid cell mediated neuroprotection after rodent spinal cord contusion. Experimental Neurology. 352. 114035–114035. 3 indexed citations
7.
Yahn, Stephanie L., et al.. (2022). Mild therapeutic hypothermia protects against inflammatory and proapoptotic processes in the rat model of cochlear implant trauma. Hearing Research. 428. 108680–108680. 7 indexed citations
8.
Milich, Lindsay M., James S. Choi, Susana R. Cerqueira, et al.. (2021). Single-cell analysis of the cellular heterogeneity and interactions in the injured mouse spinal cord. The Journal of Experimental Medicine. 218(8). 171 indexed citations
9.
Penas, Clara, Vasileios Stathias, Jun Long, et al.. (2019). Time series modeling of cell cycle exit identifies Brd4 dependent regulation of cerebellar neurogenesis. Nature Communications. 10(1). 3028–3028. 22 indexed citations
10.
Choi, James S., et al.. (2018). Bromodomain and extraterminal domain-containing protein inhibition attenuates acute inflammation after spinal cord injury. Experimental Neurology. 309. 181–192. 39 indexed citations
11.
Zhu, Yunjiao, Kirill A. Lyapichev, Dario Motti, et al.. (2017). Macrophage Transcriptional Profile Identifies Lipid Catabolic Pathways That Can Be Therapeutically Targeted after Spinal Cord Injury. Journal of Neuroscience. 37(9). 2362–2376. 85 indexed citations
12.
Kim, Young‐Min, Dong Hoon Hwang, Yuexian Cui, et al.. (2017). An injectable hydrogel enhances tissue repair after spinal cord injury by promoting extracellular matrix remodeling. Nature Communications. 8(1). 533–533. 212 indexed citations
13.
Soderblom, Cynthia, Do-Hun Lee, Andrea J. Santamaría, et al.. (2015). 3D Imaging of Axons in Transparent Spinal Cords from Rodents and Nonhuman Primates. eNeuro. 2(2). ENEURO.0001–15.2015. 54 indexed citations
15.
Zhu, Yunjiao, et al.. (2014). Hematogenous macrophage depletion reduces the fibrotic scar and increases axonal growth after spinal cord injury. Neurobiology of Disease. 74. 114–125. 161 indexed citations
16.
Blackmore, Murray G., Zimei Wang, Jessica K. Lerch, et al.. (2012). Krüppel-like Factor 7 engineered for transcriptional activation promotes axon regeneration in the adult corticospinal tract. Proceedings of the National Academy of Sciences. 109(19). 7517–7522. 205 indexed citations
17.
Lee, Jae K., et al.. (2010). Combined Genetic Attenuation of Myelin and Semaphorin-Mediated Growth Inhibition Is Insufficient to Promote Serotonergic Axon Regeneration. Journal of Neuroscience. 30(32). 10899–10904. 59 indexed citations
18.
Park, Taesung, et al.. (2008). Response projected clustering for direct association with physiological and clinical response data. BMC Bioinformatics. 9(1). 76–76. 7 indexed citations
19.
Zheng, Binhai, Jae K. Lee, & Fang Xie. (2006). Genetic mouse models for studying inhibitors of spinal axon regeneration. Trends in Neurosciences. 29(11). 640–646. 43 indexed citations
20.
Meyerhoff, James L., et al.. (2004). Lipoic acid pretreatment attenuates ferric chloride-induced seizures in the rat. Brain Research. 1016(2). 139–144. 23 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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