Jae Young Kwon

4.5k total citations
69 papers, 3.2k citations indexed

About

Jae Young Kwon is a scholar working on Cellular and Molecular Neuroscience, Immunology and Insect Science. According to data from OpenAlex, Jae Young Kwon has authored 69 papers receiving a total of 3.2k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Cellular and Molecular Neuroscience, 18 papers in Immunology and 18 papers in Insect Science. Recurrent topics in Jae Young Kwon's work include Neurobiology and Insect Physiology Research (37 papers), Insect Utilization and Effects (16 papers) and Invertebrate Immune Response Mechanisms (14 papers). Jae Young Kwon is often cited by papers focused on Neurobiology and Insect Physiology Research (37 papers), Insect Utilization and Effects (16 papers) and Invertebrate Immune Response Mechanisms (14 papers). Jae Young Kwon collaborates with scholars based in South Korea, United States and Switzerland. Jae Young Kwon's co-authors include John R. Carlson, Anupama Dahanukar, Linnea A. Weiss, Scott A. Kreher, Jeongho Park, Diya Banerjee, Min Sung Choi, Junho Lee, KyeongJin Kang and Seok Jun Moon and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Jae Young Kwon

67 papers receiving 3.2k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jae Young Kwon South Korea	29	2.2k	1.2k	881	565	498	69	3.2k
Anupama Dahanukar United States	26	2.8k 1.3×	1.5k 1.3×	1.2k 1.4×	556 1.0×	811 1.6×	44	3.5k
Greg S. B. Suh United States	21	1.8k 0.8×	672 0.6×	829 0.9×	845 1.5×	205 0.4×	31	3.0k
Verônica Rodrigues India	35	2.3k 1.0×	745 0.6×	844 1.0×	1.1k 2.0×	362 0.7×	78	3.4k
Coral G. Warr Australia	23	3.1k 1.4×	1.9k 1.6×	1.5k 1.7×	607 1.1×	453 0.9×	50	3.9k
Carlos Ribeiro Portugal	29	1.6k 0.7×	1.2k 1.0×	865 1.0×	1.2k 2.2×	177 0.4×	49	3.8k
Hubert Amrein United States	32	4.6k 2.0×	2.3k 2.0×	2.3k 2.6×	1.2k 2.2×	846 1.7×	48	6.0k
Dean P. Smith United States	33	3.3k 1.5×	2.0k 1.7×	1.7k 1.9×	1.9k 3.4×	142 0.3×	70	5.2k
Liliane Abuin Switzerland	21	2.0k 0.9×	735 0.6×	899 1.0×	979 1.7×	143 0.3×	30	2.6k
Toshiro Aigaki Japan	39	1.9k 0.9×	1.1k 0.9×	1.4k 1.5×	2.6k 4.7×	137 0.3×	138	5.5k
Laurence J. Zwiebel United States	45	5.1k 2.3×	3.6k 3.1×	2.8k 3.2×	1.1k 1.9×	130 0.3×	88	7.1k

Countries citing papers authored by Jae Young Kwon

Since Specialization

Citations

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

Fields of papers citing papers by Jae Young Kwon

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jae Young Kwon

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

All Works

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20 of 20 papers shown

Fritsch, Cornelia, et al.. (2025). Food hardness preference reveals multisensory contributions of fly larval gustatory organs in behaviour and physiology. PLoS Biology. 23(1). e3002730–e3002730. 2 indexed citations

Park, Se Hoon, et al.. (2024). Drosophila HCN mediates gustatory homeostasis by preserving sensillar transepithelial potential in sweet environments. eLife. 13. 1 indexed citations

Kim, Young‐Joon, et al.. (2024). Molecular and cellular organization of odorant binding protein genes in Drosophila. Heliyon. 10(9). e29358–e29358. 1 indexed citations

Maier, Gernot, et al.. (2021). Taste sensing and sugar detection mechanisms in Drosophila larval primary taste center. eLife. 10. 6 indexed citations

You, Hye Jin, Jae‐Jin Lee, Kyung‐Ah Lee, et al.. (2020). Identification and characterization of GAL4 drivers that mark distinct cell types and regions in the Drosophila adult gut. Journal of Neurogenetics. 35(1). 33–44. 7 indexed citations

Kwon, Jae Young, Un Ju Jung, Dong Woon Kim, et al.. (2018). Beneficial Effects of Hesperetin in a Mouse Model of Temporal Lobe Epilepsy. Journal of Medicinal Food. 21(12). 1306–1309. 23 indexed citations

Itoh, Taichi Q., et al.. (2017). Deciphering the Genes for Taste Receptors for Fructose in Drosophila. Molecules and Cells. 40(10). 731–736. 9 indexed citations

Seo, Dae‐Won, Duk L. Na, Jae Young Kwon, et al.. (2016). Nucleophile sensitivity of Drosophila TRPA1 underlies light-induced feeding deterrence. eLife. 5. 27 indexed citations

Giesen, Lena van, et al.. (2016). A microfluidics-based method for measuring neuronal activity in Drosophila chemosensory neurons. Nature Protocols. 11(12). 2389–2400. 20 indexed citations

10.

Giesen, Lena van, et al.. (2016). A Pair of Pharyngeal Gustatory Receptor Neurons Regulates Caffeine-Dependent Ingestion in Drosophila Larvae. Frontiers in Cellular Neuroscience. 10. 181–181. 26 indexed citations

11.

Chen, Ji, et al.. (2016). A Systematic Analysis of Drosophila Regulatory Peptide Expression in Enteroendocrine Cells. Molecules and Cells. 39(4). 358–366. 53 indexed citations

12.

Chen, Ji, Min Sung Choi, Akira Mizoguchi, et al.. (2015). Isoform-specific expression of the neuropeptide orcokinin in Drosophila melanogaster. Peptides. 68. 50–57. 32 indexed citations

13.

Choi, Min Sung, et al.. (2015). The Mosquito Repellent Citronellal Directly Potentiates Drosophila TRPA1, Facilitating Feeding Suppression. Molecules and Cells. 38(10). 911–917. 37 indexed citations

14.

Choi, Min Sung, et al.. (2015). Behavioral Analysis of Bitter Taste Perception inDrosophilaLarvae. Chemical Senses. 41(1). 85–94. 21 indexed citations

15.

Bae, Young‐Ki, Jee Young Sung, Yong-Nyun Kim, et al.. (2012). An In Vivo C. elegans Model System for Screening EGFR-Inhibiting Anti-Cancer Drugs. PLoS ONE. 7(9). e42441–e42441. 27 indexed citations

16.

Kim, Jun Hwan, Ki Sa Sung, Su Myung Jung, et al.. (2010). Pellino-1, an Adaptor Protein of Interleukin-1 Receptor/Toll-like Receptor Signaling, Is Sumoylated by Ubc9. Molecules and Cells. 31(1). 85–90. 12 indexed citations

17.

Lee, Youn Sook, Jun Hwan Kim, Jae Young Kwon, et al.. (2010). Smad7 and Smad6 bind to discrete regions of Pellino-1 via their MH2 domains to mediate TGF-β1-induced negative regulation of IL-1R/TLR signaling. Biochemical and Biophysical Research Communications. 393(4). 836–843. 41 indexed citations

18.

Lee, Ha Young, Sang Doo Kim, Jae Woong Shim, et al.. (2010). Activation of human monocytes by a formyl peptide receptor 2‐derived pepducin. FEBS Letters. 584(18). 4102–4108. 28 indexed citations

19.

Kwon, Junhye, et al.. (2010). A Novel LZAP-binding Protein, NLBP, Inhibits Cell Invasion. Journal of Biological Chemistry. 285(16). 12232–12240. 51 indexed citations

20.

Dahanukar, Anupama, et al.. (2007). Two Gr Genes Underlie Sugar Reception in Drosophila. Neuron. 56(3). 503–516. 349 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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