Sung Ung Kang

1.3k total citations
22 papers, 479 citations indexed

About

Sung Ung Kang is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Physiology. According to data from OpenAlex, Sung Ung Kang has authored 22 papers receiving a total of 479 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 10 papers in Cellular and Molecular Neuroscience and 5 papers in Physiology. Recurrent topics in Sung Ung Kang's work include Neuroscience and Neuropharmacology Research (6 papers), Alzheimer's disease research and treatments (4 papers) and Mass Spectrometry Techniques and Applications (4 papers). Sung Ung Kang is often cited by papers focused on Neuroscience and Neuropharmacology Research (6 papers), Alzheimer's disease research and treatments (4 papers) and Mass Spectrometry Techniques and Applications (4 papers). Sung Ung Kang collaborates with scholars based in Austria, United States and United Kingdom. Sung Ung Kang's co-authors include Gert Lübec, Friedrich Altmann, Mark Kotter, Alexandra Baer, Yasir Ahmed Syed, Charles ffrench‐Constant, Robin J.M. Franklin, Dieter Mitteregger, Karoline Fuchs and Seok Heo and has published in prestigious journals such as Brain, Journal of Proteome Research and Photochemistry and Photobiology.

In The Last Decade

Sung Ung Kang

22 papers receiving 471 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sung Ung Kang Austria 11 262 159 111 84 61 22 479
Laurie Robak United States 7 266 1.0× 270 1.7× 137 1.2× 78 0.9× 59 1.0× 11 583
Diogo Trigo Portugal 13 344 1.3× 231 1.5× 60 0.5× 112 1.3× 141 2.3× 18 654
Daniel Perrelet Switzerland 9 357 1.4× 262 1.6× 80 0.7× 43 0.5× 75 1.2× 9 620
Jingwen Niu China 14 250 1.0× 164 1.0× 60 0.5× 66 0.8× 96 1.6× 31 563
Eric Cho Hong Kong 16 258 1.0× 295 1.9× 154 1.4× 49 0.6× 83 1.4× 35 686
Martin Larhammar Sweden 10 191 0.7× 133 0.8× 42 0.4× 35 0.4× 59 1.0× 12 405
Agata Habas United States 9 339 1.3× 274 1.7× 146 1.3× 74 0.9× 61 1.0× 11 574
Joanna A. Korecka Netherlands 12 273 1.0× 248 1.6× 119 1.1× 70 0.8× 105 1.7× 14 616
Trisha R. Stankiewicz United States 8 347 1.3× 136 0.9× 42 0.4× 46 0.5× 69 1.1× 9 548

Countries citing papers authored by Sung Ung Kang

Since Specialization
Citations

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

Fields of papers citing papers by Sung Ung Kang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sung Ung Kang

This figure shows the co-authorship network connecting the top 25 collaborators of Sung Ung Kang. A scholar is included among the top collaborators of Sung Ung 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 Sung Ung Kang. Sung Ung 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.
Pirooznia, Sheila K., Changqing Yuan, Mohammed Repon Khan, et al.. (2020). PARIS induced defects in mitochondrial biogenesis drive dopamine neuron loss under conditions of parkin or PINK1 deficiency. Molecular Neurodegeneration. 15(1). 17–17. 66 indexed citations
2.
Lee, Sehyun, et al.. (2020). Molecular Crosstalk Between Circadian Rhythmicity and the Development of Neurodegenerative Disorders. Frontiers in Neuroscience. 14. 844–844. 15 indexed citations
3.
Kang, Sung Ung & Joon Tae Park. (2018). Functional evaluation of alternative splicing in the FAM190A gene. Genes & Genomics. 41(2). 193–199. 4 indexed citations
4.
Kang, Sung Ung, Seok Heo, & Gert Lübec. (2013). In-gel total protein quantification using a ninhydrin-based method. Amino Acids. 45(4). 1003–1013. 1 indexed citations
5.
Kang, Sung Ung, Seok Heo, & Gert Lübec. (2011). Mass spectrometric analysis of GABAA receptor subtypes and phosphorylations from mouse hippocampus. PROTEOMICS. 11(11). 2171–2181. 20 indexed citations
6.
Kang, Sung Ung & Gert Lübec. (2010). Determination of in‐gel protein concentration by a ninhydrin‐based method. PROTEOMICS. 11(3). 481–484. 4 indexed citations
7.
Burgos, Miguel, Noelia Fradejas‐Villar, Soledad Calvo, et al.. (2010). A proteomic analysis of PKCε targets in astrocytes: implications for astrogliosis. Amino Acids. 40(2). 641–651. 11 indexed citations
8.
Patil, Sudarshan, Konstantin Schlick, Sung Ung Kang, et al.. (2010). Differences in hippocampal protein levels between C57Bl/6J, PWD/PhJ, and Apodemus sylvaticus are paralleled by differences in spatial memory. Hippocampus. 21(7). 714–723. 5 indexed citations
9.
Heo, Seok, Sung Ung Kang, Rudolf Oehler, Arnold Pollak, & Gert Lübec. (2010). Mass spectrometrical analysis of the mitochondrial carrier Aralar1 from mouse hippocampus. Electrophoresis. 31(11). 1813–1821. 6 indexed citations
10.
Li, Lin, et al.. (2010). Olfactory bulb proteins linked to olfactory memory in C57BL/6J mice. Amino Acids. 39(3). 871–886. 13 indexed citations
11.
Kang, Sung Ung & Gert Lübec. (2009). Complete sequencing of GABAA receptor subunit β3 by a rapid technique following in‐gel digestion of the protein. Electrophoresis. 30(12). 2159–2167. 8 indexed citations
12.
Sunyer, Berta, et al.. (2009). Strain-dependent hippocampal protein levels of GABAB-receptor subunit 2 and NMDA-receptor subunit 1. Neurochemistry International. 55(4). 253–256. 9 indexed citations
13.
14.
Ströbel, Thomas, Sung Ung Kang, Julius Paul Pradeep John, et al.. (2009). Neurotrophin 3/TrkC‐regulated proteins in the human medulloblastoma cell line DAOY. Electrophoresis. 30(3). 540–549. 7 indexed citations
15.
Baer, Alexandra, Yasir Ahmed Syed, Sung Ung Kang, et al.. (2009). Myelin-mediated inhibition of oligodendrocyte precursor differentiation can be overcome by pharmacological modulation of Fyn-RhoA and protein kinase C signalling. Brain. 132(2). 465–481. 173 indexed citations
16.
Kang, Sung Ung, et al.. (2009). Mass spectrometrical characterisation of mouse and rat synapsin isoforms 2a and 2b. Amino Acids. 38(4). 1131–1143. 2 indexed citations
17.
Wang, Chaozhan, Matti Myllykoski, Salla M. Kangas, et al.. (2009). Structural analysis of the complex between calmodulin and full-length myelin basic protein, an intrinsically disordered molecule. Amino Acids. 39(1). 59–71. 41 indexed citations
18.
Kang, Sung Ung, Karoline Fuchs, Werner Sieghart, & Gert Lübec. (2008). Gel-Based Mass Spectrometric Analysis of Recombinant GABAAReceptor Subunits Representing Strongly Hydrophobic Transmembrane Proteins. Journal of Proteome Research. 7(8). 3498–3506. 30 indexed citations
19.
Sunyer, Berta, et al.. (2008). Cognitive Enhancement by SGS742 in OF1 Mice Is Linked to Specific Hippocampal Protein Expression. Journal of Proteome Research. 7(12). 5237–5253. 16 indexed citations
20.
Bierczyńska-Krzysik, Anna, et al.. (2006). Mass spectrometrical identification of brain proteins including highly insoluble and transmembrane proteins. Neurochemistry International. 49(3). 245–255. 13 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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