Yeona Kang

740 total citations
30 papers, 460 citations indexed

About

Yeona Kang is a scholar working on Molecular Biology, Neurology and Cellular and Molecular Neuroscience. According to data from OpenAlex, Yeona Kang has authored 30 papers receiving a total of 460 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 7 papers in Neurology and 6 papers in Cellular and Molecular Neuroscience. Recurrent topics in Yeona Kang's work include Multiple Sclerosis Research Studies (5 papers), Protein Structure and Dynamics (3 papers) and Parkinson's Disease Mechanisms and Treatments (3 papers). Yeona Kang is often cited by papers focused on Multiple Sclerosis Research Studies (5 papers), Protein Structure and Dynamics (3 papers) and Parkinson's Disease Mechanisms and Treatments (3 papers). Yeona Kang collaborates with scholars based in United States, France and Canada. Yeona Kang's co-authors include Susan A. Gauthier, P. David Mozley, Ulrike W. Kaunzner, Sneha Pandya, Sandra Hurtado Rúa, Claire Henchcliffe, Joanna S. Fowler, Kelly M. Gillen, Nora D. Volkow and Shun Zhang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and PLoS ONE.

In The Last Decade

Yeona Kang

26 papers receiving 455 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yeona Kang United States 10 159 132 116 97 81 30 460
Isabel Benzel United Kingdom 9 295 1.9× 104 0.8× 48 0.4× 183 1.9× 42 0.5× 10 686
Gabriela Kłodowska-Duda Poland 14 178 1.1× 68 0.5× 348 3.0× 160 1.6× 105 1.3× 20 641
Ana Cavey United Kingdom 11 195 1.2× 256 1.9× 272 2.3× 94 1.0× 51 0.6× 19 632
Gen Yan China 17 116 0.7× 40 0.3× 53 0.5× 97 1.0× 76 0.9× 49 639
Benoît Aubé Canada 9 206 1.3× 71 0.5× 157 1.4× 145 1.5× 138 1.7× 10 630
Anna Maio Italy 15 198 1.2× 38 0.3× 102 0.9× 185 1.9× 27 0.3× 45 621
Dragan Marić Croatia 12 160 1.0× 98 0.7× 48 0.4× 166 1.7× 52 0.6× 21 427
Damien Bochelen Switzerland 12 177 1.1× 52 0.4× 43 0.4× 132 1.4× 121 1.5× 15 593
Chitra Joseph Australia 7 141 0.9× 31 0.2× 42 0.4× 82 0.8× 51 0.6× 9 370
Kazunori Nanri Japan 12 137 0.9× 46 0.3× 293 2.5× 270 2.8× 80 1.0× 28 581

Countries citing papers authored by Yeona Kang

Since Specialization
Citations

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

Fields of papers citing papers by Yeona Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yeona Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Yeona Kang. A scholar is included among the top collaborators of Yeona Kang 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 Yeona Kang. Yeona Kang 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.
Ramsey, J Tyler, Abolghasem Bakhoda, Min Guo, et al.. (2024). Investigation of [11C]carfentanil for mu opioid receptor quantification in the rat brain. Scientific Reports. 14(1). 16250–16250. 3 indexed citations
2.
Kim, Ryan, Chao‐Hsiung Hsu, Scott Love, et al.. (2022). A Baboon Brain Atlas for Magnetic Resonance Imaging and Positron Emission Tomography Image Analysis. Frontiers in Neuroanatomy. 15. 778769–778769. 3 indexed citations
3.
Kang, Yeona, J Tyler Ramsey, Abolghasem Bakhoda, et al.. (2022). Naloxone’s dose-dependent displacement of [11C]carfentanil and duration of receptor occupancy in the rat brain. Scientific Reports. 12(1). 6429–6429. 17 indexed citations
4.
Kang, Yeona, et al.. (2021). Longitudinal change in TSPO PET imaging in progressive multiple sclerosis. Annals of Clinical and Translational Neurology. 8(8). 1755–1759. 11 indexed citations
5.
Guo, Min, Abolghasem Bakhoda, Zhan‐Guo Gao, et al.. (2021). Discovery of Highly Potent Adenosine A1 Receptor Agonists: Targeting Positron Emission Tomography Probes. ACS Chemical Neuroscience. 12(18). 3410–3417. 4 indexed citations
6.
Berka, Chris, Marija Stevanović Karić, P. David Mozley, et al.. (2020). Neurophysiological Biomarkers of Parkinson’s Disease. Journal of Parkinson s Disease. 10(2). 471–480. 58 indexed citations
7.
Kang, Yeona, Sandra Hurtado Rúa, Ulrike W. Kaunzner, et al.. (2020). A Multi-Ligand Imaging Study Exploring GABAergic Receptor Expression and Inflammation in Multiple Sclerosis. Molecular Imaging and Biology. 22(6). 1600–1608. 5 indexed citations
8.
Kang, Yeona, et al.. (2019). Risk structured model of cholera infections in Cameroon. Mathematical Biosciences. 320. 108303–108303. 7 indexed citations
9.
Kang, Yeona, P. David Mozley, Ajay Verma, et al.. (2018). Noninvasive PK11195‐PET Image Analysis Techniques Can Detect Abnormal Cerebral Microglial Activation in Parkinson's Disease. Journal of Neuroimaging. 28(5). 496–505. 28 indexed citations
10.
Kang, Yeona, David J. Schlyer, Ulrike W. Kaunzner, et al.. (2018). Comparison of two different methods of image analysis for the assessment of microglial activation in patients with multiple sclerosis using (R)-[N-methyl-carbon-11]PK11195. PLoS ONE. 13(8). e0201289–e0201289. 7 indexed citations
11.
Kang, Yeona, Claire Henchcliffe, Ajay Verma, et al.. (2018). 18F‐FPEB PET/CT Shows mGluR5 Upregulation in Parkinson's Disease. Journal of Neuroimaging. 29(1). 97–103. 26 indexed citations
12.
Kaunzner, Ulrike W., Yeona Kang, Paresh J. Kothari, et al.. (2017). Reduction of PK11195 uptake observed in multiple sclerosis lesions after natalizumab initiation. Multiple Sclerosis and Related Disorders. 15. 27–33. 26 indexed citations
13.
Kim, Sung Won, Joanna S. Fowler, Phil Skolnick, et al.. (2014). Therapeutic doses of buspirone block D3 receptors in the living primate brain. The International Journal of Neuropsychopharmacology. 17(8). 1257–1267. 27 indexed citations
14.
Seo, Young Jun, Yeona Kang, Lisa Muench, et al.. (2014). Image-Guided Synthesis Reveals Potent Blood-Brain Barrier Permeable Histone Deacetylase Inhibitors. ACS Chemical Neuroscience. 5(7). 588–596. 49 indexed citations
15.
Seo, Young Jun, Lisa Muench, Alicia E. Reid, et al.. (2013). Radionuclide labeling and evaluation of candidate radioligands for PET imaging of histone deacetylase in the brain. Bioorganic & Medicinal Chemistry Letters. 23(24). 6700–6705. 27 indexed citations
16.
Kang, Yeona & C.M. Fortmann. (2013). An Alternative Approach to Protein Folding. BioMed Research International. 2013. 1–10. 3 indexed citations
17.
Lee, Ping, et al.. (2012). Enhanced Chlorophyll A Purification and Dye Sensitized Solar Cell Performance. MRS Proceedings. 1390. 2 indexed citations
18.
Lee, Ping, et al.. (2011). Crystal particle Raman-scattering and applications for improved solar cell performance. Applied Physics Letters. 99(25). 2 indexed citations
19.
Kang, Yeona, et al.. (2009). Physical Markov model for protein structure prediction. 356–356.
20.
Kang, Yeona, et al.. (2006). Einstein relations for energy coupled particle systems. Applied Physics Letters. 88(11). 3 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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