Young V. Kwon

2.3k total citations
33 papers, 1.7k citations indexed

About

Young V. Kwon is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Immunology. According to data from OpenAlex, Young V. Kwon has authored 33 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Molecular Biology, 11 papers in Cellular and Molecular Neuroscience and 7 papers in Immunology. Recurrent topics in Young V. Kwon's work include Neurobiology and Insect Physiology Research (11 papers), Hippo pathway signaling and YAP/TAZ (5 papers) and Muscle Physiology and Disorders (4 papers). Young V. Kwon is often cited by papers focused on Neurobiology and Insect Physiology Research (11 papers), Hippo pathway signaling and YAP/TAZ (5 papers) and Muscle Physiology and Disorders (4 papers). Young V. Kwon collaborates with scholars based in United States, South Korea and Germany. Young V. Kwon's co-authors include Craig Montell, Norbert Perrimon, Thomas Hofmann, Wei L. Shen, Yanhui Hu, Arunachalam Vinayagam, Xiaoyue Wang, Ilia A. Droujinine, John M. Asara and Wei Song and has published in prestigious journals such as Science, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Young V. Kwon

30 papers receiving 1.6k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Young V. Kwon United States 16 753 639 249 244 205 33 1.7k
Kathy Beckingham United States 22 1.5k 2.0× 730 1.1× 225 0.9× 139 0.6× 159 0.8× 40 2.1k
Bih‐Hwa Shieh United States 19 1.2k 1.6× 746 1.2× 259 1.0× 149 0.6× 78 0.4× 31 1.8k
Padinjat Raghu India 26 1.4k 1.9× 1.3k 2.1× 699 2.8× 561 2.3× 132 0.6× 64 2.5k
Greg L. Harris United States 21 1.1k 1.5× 450 0.7× 228 0.9× 66 0.3× 96 0.5× 26 1.6k
Ching‐On Wong United States 21 664 0.9× 223 0.3× 245 1.0× 537 2.2× 108 0.5× 32 1.6k
Natasha Thorne United States 18 1.2k 1.7× 771 1.2× 107 0.4× 138 0.6× 297 1.4× 29 2.4k
Khaled Machaca Qatar 31 1.3k 1.8× 532 0.8× 294 1.2× 742 3.0× 45 0.2× 94 2.6k
Patrick J. Dolph United States 21 1.4k 1.8× 776 1.2× 388 1.6× 42 0.2× 66 0.3× 36 1.9k
Yogarany Chelliah United States 17 754 1.0× 489 0.8× 139 0.6× 33 0.1× 305 1.5× 19 1.7k
Franz-Josef Braun Germany 11 720 1.0× 407 0.6× 95 0.4× 402 1.6× 97 0.5× 12 1.2k

Countries citing papers authored by Young V. Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Young V. Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young V. Kwon. 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 Young V. Kwon. The network helps show where Young V. Kwon may publish in the future.

Co-authorship network of co-authors of Young V. Kwon

This figure shows the co-authorship network connecting the top 25 collaborators of Young V. Kwon. A scholar is included among the top collaborators of Young V. Kwon 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 Young V. Kwon. Young V. Kwon 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.
Kim, Soon‐Young, Hee‐Joo Choi, Jeong‐Yeon Lee, et al.. (2024). Comparative interactome analysis of α-arrestin families in human and Drosophila. eLife. 12.
2.
3.
Gumbiner, Barry M., et al.. (2023). Remodeling of E-cadherin subcellular localization during cell dissemination. Molecular Biology of the Cell. 34(5). ar46–ar46. 7 indexed citations
4.
Gell, L., Ludovico Messineo, Daniel Vena, et al.. (2023). Arousal Intensity Predicts Incident Dementia in Sleep Apnea: The Multiethnic Study of Atherosclerosis (MESA). A5970–A5970.
5.
Kwon, Young V., et al.. (2023). The roles of the native cell differentiation program aberrantly recapitulated in Drosophila intestinal tumors. Cell Reports. 42(10). 113245–113245. 1 indexed citations
6.
Kim, Soon‐Young, Hee‐Joo Choi, Jeong‐Yeon Lee, et al.. (2023). Comparative interactome analysis of α-arrestin families in human and Drosophila. eLife. 12. 1 indexed citations
7.
Jang, Jaewoong, Jaewon Song, Jong‐Kyu Lee, et al.. (2022). Paeonia lactiflora extract improves the muscle function of mdx mice, an animal model of Duchenne muscular dystrophy, via downregulating the high mobility group box 1 protein. Journal of Ethnopharmacology. 289. 115079–115079. 2 indexed citations
8.
Wang, Yi, et al.. (2022). Wear and tear of the intestinal visceral musculature by intrinsic and extrinsic factors. Developmental Dynamics. 251(8). 1291–1305. 2 indexed citations
9.
Dombek, Kenneth M., et al.. (2021). Tumors overcome the action of the wasting factor ImpL2 by locally elevating Wnt/Wingless. Proceedings of the National Academy of Sciences. 118(23). 22 indexed citations
10.
11.
Jang, Jaewoong, Hyunji Lee, Jaewon Song, et al.. (2020). Paeonia lactiflora extract suppresses cisplatin-induced muscle wasting via downregulation of muscle-specific ubiquitin E3 ligases, NF-κB signaling, and cytokine levels. Journal of Ethnopharmacology. 266. 113403–113403. 6 indexed citations
12.
Kwon, Young V., et al.. (2016). Drosophila melanogaster as a Model of Muscle Degeneration Disorders. Current topics in developmental biology. 121. 83–109. 28 indexed citations
13.
Kwon, Young V., Wei Song, Ilia A. Droujinine, et al.. (2015). Systemic Organ Wasting Induced by Localized Expression of the Secreted Insulin/IGF Antagonist ImpL2. Developmental Cell. 33(1). 36–46. 191 indexed citations
14.
Shen, Wei L., Young V. Kwon, Abidemi Adegbola, et al.. (2011). Function of Rhodopsin in Temperature Discrimination in Drosophila. Science. 331(6022). 1333–1336. 184 indexed citations
15.
Kwon, Young V., Sang Hoon Kim, Youngseok Lee, et al.. (2010). Drosophila TRPA1 Channel Is Required to Avoid the Naturally Occurring Insect Repellent Citronellal. Current Biology. 20(18). 1672–1678. 158 indexed citations
16.
Kwon, Young V., et al.. (2010). Fine Thermotactic Discrimination between the Optimal and Slightly Cooler Temperatures via a TRPV Channel in Chordotonal Neurons. Journal of Neuroscience. 30(31). 10465–10471. 88 indexed citations
17.
Kwon, Young V., et al.. (2008). Control of thermotactic behavior via coupling of a TRP channel to a phospholipase C signaling cascade. Nature Neuroscience. 11(8). 871–873. 143 indexed citations
18.
Kwon, Young V., Thomas Hofmann, & Craig Montell. (2007). Integration of Phosphoinositide- and Calmodulin-Mediated Regulation of TRPC6. Molecular Cell. 25(4). 491–503. 176 indexed citations
19.
Kwon, Young V. & Craig Montell. (2006). Dependence on the Lazaro Phosphatidic Acid Phosphatase for the Maximum Light Response. Current Biology. 16(7). 723–729. 28 indexed citations
20.
Kwon, Young V., et al.. (2000). The p53 Tumor Suppressor Stimulates the Catalytic Activity of Human Topoisomerase IIα by Enhancing the Rate of ATP Hydrolysis. Journal of Biological Chemistry. 275(24). 18503–18510. 14 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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