Yumi Kwon

920 total citations
27 papers, 399 citations indexed

About

Yumi Kwon is a scholar working on Molecular Biology, Spectroscopy and Genetics. According to data from OpenAlex, Yumi Kwon has authored 27 papers receiving a total of 399 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 10 papers in Spectroscopy and 6 papers in Genetics. Recurrent topics in Yumi Kwon's work include Advanced Proteomics Techniques and Applications (10 papers), Mass Spectrometry Techniques and Applications (5 papers) and Tuberculosis Research and Epidemiology (4 papers). Yumi Kwon is often cited by papers focused on Advanced Proteomics Techniques and Applications (10 papers), Mass Spectrometry Techniques and Applications (5 papers) and Tuberculosis Research and Epidemiology (4 papers). Yumi Kwon collaborates with scholars based in South Korea, United States and Australia. Yumi Kwon's co-authors include Cheolju Lee, Sungho Shin, Jae‐Hoon Jeong, Kang-Sik Park, Sa Ik Bang, Suk‐Joo Choi, Jeongmin Lee, Jong Wook Chang, Jin‐Won Lee and Shinyeong Ju and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and SHILAP Revista de lepidopterología.

In The Last Decade

Yumi Kwon

27 papers receiving 392 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yumi Kwon South Korea 12 196 91 57 53 52 27 399
Francesca Gasparrini United Kingdom 15 201 1.0× 30 0.3× 17 0.3× 74 1.4× 24 0.5× 17 569
Melinda L. Ramsby United States 14 262 1.3× 16 0.2× 63 1.1× 99 1.9× 35 0.7× 21 598
Katie M. Kuo United States 11 162 0.8× 33 0.4× 9 0.2× 53 1.0× 39 0.8× 16 342
Maurizia DʼEgidio Italy 12 388 2.0× 15 0.2× 16 0.3× 170 3.2× 35 0.7× 15 691
Devin Bready United States 7 276 1.4× 92 1.0× 4 0.1× 63 1.2× 44 0.8× 11 449
Byung‐Chul Lee South Korea 14 346 1.8× 40 0.4× 5 0.1× 59 1.1× 29 0.6× 29 552
Valentina Dubljevic Australia 12 189 1.0× 68 0.7× 5 0.1× 74 1.4× 13 0.3× 19 505
Deanne Hebrink United States 12 517 2.6× 161 1.8× 8 0.1× 118 2.2× 26 0.5× 16 785
R. Katherine Hyde United States 15 349 1.8× 35 0.4× 10 0.2× 108 2.0× 9 0.2× 30 562
Linda Cashion United States 12 303 1.5× 14 0.2× 10 0.2× 35 0.7× 52 1.0× 18 469

Countries citing papers authored by Yumi Kwon

Since Specialization
Citations

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

Fields of papers citing papers by Yumi Kwon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yumi Kwon

This figure shows the co-authorship network connecting the top 25 collaborators of Yumi Kwon. A scholar is included among the top collaborators of Yumi 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 Yumi Kwon. Yumi 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.
Ju, Shinyeong, Sung‐Ho Goh, Byoung-Ha Yoon, et al.. (2024). Proteogenomic Characterization Reveals Estrogen Signaling as a Target for Never-Smoker Lung Adenocarcinoma Patients without EGFR or ALK Alterations. Cancer Research. 84(9). 1491–1503. 7 indexed citations
2.
Kwon, Yumi, Jongmin Jacob Woo, Fengchao Yu, et al.. (2024). Proteome-Scale Tissue Mapping Using Mass Spectrometry Based on Label-Free and Multiplexed Workflows. Molecular & Cellular Proteomics. 23(11). 100841–100841. 7 indexed citations
3.
Park, Na Rae, Yumi Kwon, Shinyeong Ju, et al.. (2023). One-STAGE Tip Method for TMT-Based Proteomic Analysis of a Minimal Amount of Cells. ACS Omega. 8(22). 19741–19751. 2 indexed citations
4.
Liyu, Andrey, Yumi Kwon, Dehong Hu, et al.. (2023). Spatial Proteomics toward Subcellular Resolution by Coupling Deep Ultraviolet Laser Ablation with Nanodroplet Sample Preparation. SHILAP Revista de lepidopterología. 3(6). 459–468. 13 indexed citations
5.
Kwon, Yumi, Paul Piehowski, Rui Zhao, et al.. (2022). Hanging drop sample preparation improves sensitivity of spatial proteomics. Lab on a Chip. 22(15). 2869–2877. 17 indexed citations
6.
Shin, Sungho, Seonjeong Lee, Sun-Young Choi, et al.. (2022). Characterization of the Secretome of a Specific Cell Expressing Mutant Methionyl-tRNA Synthetase in Co-Culture Using Click Chemistry. International Journal of Molecular Sciences. 23(12). 6527–6527. 4 indexed citations
7.
Kwon, Yumi, Seongjun Park, Mi Jeong Kim, et al.. (2021). Multi-layered proteogenomic analysis unravels cancer metastasis directed by MMP-2 and focal adhesion kinase signaling. Scientific Reports. 11(1). 17130–17130. 15 indexed citations
8.
Shin, Sungho, Jeongmin Lee, Yumi Kwon, et al.. (2021). Comparative Proteomic Analysis of the Mesenchymal Stem Cells Secretome from Adipose, Bone Marrow, Placenta and Wharton’s Jelly. International Journal of Molecular Sciences. 22(2). 845–845. 129 indexed citations
9.
Shin, Jihye, et al.. (2019). Comparative analysis of differentially secreted proteins in serum-free and serum-containing media by using BONCAT and pulsed SILAC. Scientific Reports. 9(1). 3096–3096. 40 indexed citations
10.
Park, Eunjin, et al.. (2019). Dual control of RegX3 transcriptional activity by SenX3 and PknB. Journal of Biological Chemistry. 294(28). 11023–11034. 15 indexed citations
11.
Lee, Youngjin, Byoung Sik Kim, Eun‐Young Lee, et al.. (2019). Makes caterpillars floppy-like effector-containing MARTX toxins require host ADP-ribosylation factor (ARF) proteins for systemic pathogenicity. Proceedings of the National Academy of Sciences. 116(36). 18031–18040. 18 indexed citations
12.
Ju, Shinyeong, et al.. (2017). The difference in in vivo sensitivity between Bacillus licheniformis PerR and Bacillus subtilis PerR is due to the different cellular environments. Biochemical and Biophysical Research Communications. 484(1). 125–131. 7 indexed citations
13.
Kim, Jung Hoon, et al.. (2017). The inability of Bacillus licheniformis perR mutant to grow is mainly due to the lack of PerR-mediated fur repression. The Journal of Microbiology. 55(6). 457–463. 5 indexed citations
14.
Kim, Sung Dae, Minyeop Nahm, Yumi Kwon, et al.. (2017). Graf regulates hematopoiesis through GEEC endocytosis of EGFR. Development. 144(22). 4159–4172. 14 indexed citations
15.
Kim, Jung‐Hoon, et al.. (2016). Bacillus licheniformis Contains Two More PerR-Like Proteins in Addition to PerR, Fur, and Zur Orthologues. PLoS ONE. 11(5). e0155539–e0155539. 10 indexed citations
16.
Kwon, Yumi, et al.. (2014). Si PIN Radiation Sensor with CMOS Readout Circuit. Journal of Sensor Science and Technology. 23(2). 73–81. 1 indexed citations
17.
18.
Lee, Ji‐Sook, Ji Woong Son, Yumi Kwon, et al.. (2006). Ex Vivo Responses for Interferon‐gamma and Proinflammatory Cytokine Secretion to Low‐Molecular‐Weight Antigen MTB12 of Mycobacterium tuberculosis during Human Tuberculosis. Scandinavian Journal of Immunology. 64(2). 145–154. 13 indexed citations
19.
Park, Jeong‐Kyu, Jaehyun Lim, Su‐Young Kim, et al.. (2006). Identification of Proteins Induced at Hypoxic and Low pH Conditions inMycobacterium tuberculosisH37Rv. Journal of Bacteriology and Virology. 36(2). 59–59. 2 indexed citations
20.

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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