So-Yoon Won

1.8k total citations
34 papers, 1.2k citations indexed

About

So-Yoon Won is a scholar working on Cellular and Molecular Neuroscience, Molecular Biology and Neurology. According to data from OpenAlex, So-Yoon Won has authored 34 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Cellular and Molecular Neuroscience, 14 papers in Molecular Biology and 13 papers in Neurology. Recurrent topics in So-Yoon Won's work include Neuroinflammation and Neurodegeneration Mechanisms (11 papers), Parkinson's Disease Mechanisms and Treatments (10 papers) and Neuroscience and Neuropharmacology Research (7 papers). So-Yoon Won is often cited by papers focused on Neuroinflammation and Neurodegeneration Mechanisms (11 papers), Parkinson's Disease Mechanisms and Treatments (10 papers) and Neuroscience and Neuropharmacology Research (7 papers). So-Yoon Won collaborates with scholars based in South Korea, Australia and United States. So-Yoon Won's co-authors include Sang Ryong Kim, Byung Kwan Jin, Eugene Bok, Young Cheul Chung, Won-Ho Shin, Jeong Yeob Baek, Eun‐Young Shin, Eung‐Gook Kim, Min‐Tae Jeon and Kyoung Sang Cho and has published in prestigious journals such as Nature Communications, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

So-Yoon Won

33 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
So-Yoon Won South Korea 20 362 309 296 264 241 34 1.2k
Eugene Bok South Korea 18 365 1.0× 357 1.2× 443 1.5× 349 1.3× 245 1.0× 26 1.2k
Nathaniel S. Woodling United States 19 551 1.5× 207 0.7× 386 1.3× 166 0.6× 476 2.0× 32 1.4k
Zhao Zhang China 26 791 2.2× 190 0.6× 483 1.6× 181 0.7× 201 0.8× 61 1.6k
Er‐Qing Wei China 24 438 1.2× 203 0.7× 394 1.3× 110 0.4× 225 0.9× 34 1.1k
Yan Zheng China 25 801 2.2× 338 1.1× 386 1.3× 363 1.4× 460 1.9× 45 1.9k
Chun‐Yi Jiang China 21 684 1.9× 277 0.9× 304 1.0× 157 0.6× 395 1.6× 42 1.4k
Kaifu Ke China 22 628 1.7× 169 0.5× 285 1.0× 257 1.0× 248 1.0× 72 1.3k
Palwinder K. Mander United Kingdom 14 491 1.4× 290 0.9× 697 2.4× 178 0.7× 314 1.3× 20 1.6k
Parichehr Pasbakhsh Iran 21 372 1.0× 226 0.7× 301 1.0× 111 0.4× 213 0.9× 78 1.3k
Jin Han Nam South Korea 17 300 0.8× 231 0.7× 293 1.0× 177 0.7× 134 0.6× 27 854

Countries citing papers authored by So-Yoon Won

Since Specialization
Citations

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

Fields of papers citing papers by So-Yoon Won

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by So-Yoon Won. 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 So-Yoon Won. The network helps show where So-Yoon Won may publish in the future.

Co-authorship network of co-authors of So-Yoon Won

This figure shows the co-authorship network connecting the top 25 collaborators of So-Yoon Won. A scholar is included among the top collaborators of So-Yoon Won 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 So-Yoon Won. So-Yoon Won 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
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Lee, Byoung Dae, So-Yoon Won, Young Cheul Chung, et al.. (2024). Neuroprotective effect of L-DOPA-induced interleukin-13 on striatonigral degeneration in cerebral ischemia. Cell Death and Disease. 15(11). 854–854. 2 indexed citations
3.
Kim, Hee Jong, et al.. (2024). An examination of the mechanisms driving the therapeutic effects of an AAV expressing a soluble variant of VEGF receptor-1. PLoS ONE. 19(7). e0305466–e0305466. 1 indexed citations
4.
Won, So-Yoon, et al.. (2023). Neutrophil depletion for early allogeneic islet survival in a methacrylic acid (MAA) copolymer-induced, vascularized subcutaneous space. SHILAP Revista de lepidopterología. 2. 1244093–1244093. 1 indexed citations
5.
Cha, Seho, et al.. (2023). scAAV2-Mediated Expression of Thioredoxin 2 and C3 Transferase Prevents Retinal Ganglion Cell Death and Lowers Intraocular Pressure in a Mouse Model of Glaucoma. International Journal of Molecular Sciences. 24(22). 16253–16253. 1 indexed citations
6.
Won, So-Yoon, Jungjin Park, Soon-Tae You, et al.. (2022). p21-activated kinase 4 controls the aggregation of α-synuclein by reducing the monomeric and aggregated forms of α-synuclein: involvement of the E3 ubiquitin ligase NEDD4-1. Cell Death and Disease. 13(6). 575–575. 7 indexed citations
7.
Yun, Hye Sup, et al.. (2021). Linalool Alleviates Aβ42‐Induced Neurodegeneration via Suppressing ROS Production and Inflammation in Fly and Rat Models of Alzheimer’s Disease. Oxidative Medicine and Cellular Longevity. 2021(1). 8887716–8887716. 40 indexed citations
8.
Won, So-Yoon, Soon-Tae You, Seung-Won Choi, et al.. (2021). cAMP Response Element-Binding Protein- and Phosphorylation-Dependent Regulation of Tyrosine Hydroxylase by PAK4: Implications for Dopamine Replacement Therapy. Molecules and Cells. 44(7). 493–499. 3 indexed citations
9.
Choi, Jai Ho, Hyeong Cheol Moon, So-Yoon Won, et al.. (2020). An Optimization of AAV-82Q-Delivered Rat Model of Huntington’s Disease. Journal of Korean Neurosurgical Society. 63(5). 579–589. 7 indexed citations
10.
Moon, Hyeong Cheol, Soochong Kim, Hyong Kyu Kim, et al.. (2019). Optical Modulation on the Nucleus Accumbens Core in the Alleviation of Neuropathic Pain in Chronic Dorsal Root Ganglion Compression Rat Model. Neuromodulation Technology at the Neural Interface. 23(2). 167–176. 12 indexed citations
11.
Vlahos, Alexander E., et al.. (2019). Endothelialized collagen based pseudo-islets enables tuneable subcutaneous diabetes therapy. Biomaterials. 232. 119710–119710. 44 indexed citations
12.
Won, So-Yoon, et al.. (2019). PAK4 signaling in health and disease: defining the PAK4–CREB axis. Experimental & Molecular Medicine. 51(2). 1–9. 65 indexed citations
13.
Chung, Young Cheul, Jeong Yeob Baek, Sang Ryong Kim, et al.. (2017). Capsaicin prevents degeneration of dopamine neurons by inhibiting glial activation and oxidative stress in the MPTP model of Parkinson’s disease. Experimental & Molecular Medicine. 49(3). e298–e298. 89 indexed citations
14.
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16.
Kim, Byung-Wook, Kyoung Hoon Jeong, Jae‐Hong Kim, et al.. (2016). Pathogenic Upregulation of Glial Lipocalin-2 in the Parkinsonian Dopaminergic System. Journal of Neuroscience. 36(20). 5608–5622. 96 indexed citations
17.
Shin, Eun‐Young, So-Yoon Won, Hyong Kyu Kim, et al.. (2014). Non-Muscle Myosin II Regulates Neuronal Actin Dynamics by Interacting with Guanine Nucleotide Exchange Factors. PLoS ONE. 9(4). e95212–e95212. 10 indexed citations
18.
Leem, Eunju, Jin Han Nam, Min‐Tae Jeon, et al.. (2014). Naringin protects the nigrostriatal dopaminergic projection through induction of GDNF in a neurotoxin model of Parkinson's disease. The Journal of Nutritional Biochemistry. 25(7). 801–806. 78 indexed citations
19.
Chung, Young Cheul, Sang Ryong Kim, Ju Young Park, et al.. (2011). Fluoxetine prevents MPTP-induced loss of dopaminergic neurons by inhibiting microglial activation. Neuropharmacology. 60(6). 963–974. 111 indexed citations
20.
Kim, Sang Ryong, Eun Sook Chung, Eugene Bok, et al.. (2009). Prothrombin kringle‐2 induces death of mesencephalic dopaminergic neurons in vivo and in vitro via microglial activation. Journal of Neuroscience Research. 88(7). 1537–1548. 40 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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