Young‐Suk Oh

1.8k total citations
55 papers, 1.1k citations indexed

About

Young‐Suk Oh is a scholar working on Molecular Biology, Global and Planetary Change and Atmospheric Science. According to data from OpenAlex, Young‐Suk Oh has authored 55 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Molecular Biology, 16 papers in Global and Planetary Change and 15 papers in Atmospheric Science. Recurrent topics in Young‐Suk Oh's work include Ion channel regulation and function (20 papers), Atmospheric and Environmental Gas Dynamics (16 papers) and Atmospheric chemistry and aerosols (11 papers). Young‐Suk Oh is often cited by papers focused on Ion channel regulation and function (20 papers), Atmospheric and Environmental Gas Dynamics (16 papers) and Atmospheric chemistry and aerosols (11 papers). Young‐Suk Oh collaborates with scholars based in United States, South Korea and Japan. Young‐Suk Oh's co-authors include Stephen G. Waxman, Kyungsoon Chung, Jin Mo Chung, Joel A. Black, Dale Benos, Stephen G. Waxman, David G. Warnock, Karen B. Zur, Stephen G. Waxman and Sadis Matalon and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Journal of Neuroscience.

In The Last Decade

Young‐Suk Oh

48 papers receiving 1.0k 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‐Suk Oh United States 19 745 469 243 96 71 55 1.1k
Rebecca L. Miller United States 17 392 0.5× 311 0.7× 461 1.9× 33 0.3× 65 0.9× 28 1.1k
Trevor Smith United Kingdom 13 368 0.5× 313 0.7× 382 1.6× 29 0.3× 43 0.6× 20 1.1k
S. Kamm Germany 9 470 0.6× 262 0.6× 125 0.5× 145 1.5× 87 1.2× 17 912
Jason M. Keller United States 22 571 0.8× 347 0.7× 421 1.7× 124 1.3× 28 0.4× 37 1.8k
R. M. Wadsworth United Kingdom 21 380 0.5× 193 0.4× 394 1.6× 284 3.0× 40 0.6× 90 1.4k
Owen Jones United Kingdom 16 187 0.3× 355 0.8× 119 0.5× 89 0.9× 57 0.8× 39 979
Yixing Du China 13 240 0.3× 329 0.7× 65 0.3× 24 0.3× 43 0.6× 21 986
Jinbiao Zhang China 19 336 0.5× 73 0.2× 146 0.6× 32 0.3× 23 0.3× 54 991
Stephen J. Marsh United Kingdom 27 1.9k 2.6× 1.5k 3.1× 497 2.0× 498 5.2× 55 0.8× 45 2.5k

Countries citing papers authored by Young‐Suk Oh

Since Specialization
Citations

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

Fields of papers citing papers by Young‐Suk Oh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Young‐Suk Oh. 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‐Suk Oh. The network helps show where Young‐Suk Oh may publish in the future.

Co-authorship network of co-authors of Young‐Suk Oh

This figure shows the co-authorship network connecting the top 25 collaborators of Young‐Suk Oh. A scholar is included among the top collaborators of Young‐Suk Oh 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‐Suk Oh. Young‐Suk Oh 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.
Oh, Young‐Suk & Zhixiong Guo. (2024). MACHINE LEARNING-BASED PREDICTIONS OF NANOFLUID THERMAL PROPERTIES. Heat Transfer Research. 55(18). 1–26.
2.
Xu, Yongming, Shanyou Zhu, Wei Wang, et al.. (2024). Validation of Remotely Sensed XCO2 Products With TCCON Observations in East Asia. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 17. 7159–7169. 3 indexed citations
3.
Bassous, Nicole, Hyun Young Jung, Sumin Kim, et al.. (2024). Significance of Various Sensing Mechanisms for Detecting Local and Atmospheric Greenhouse Gases: A Review (Adv. Sensor Res. 2/2024). Advanced Sensor Research. 3(2).
4.
Bassous, Nicole, Hyun Young Jung, Sumin Kim, et al.. (2023). Significance of Various Sensing Mechanisms for Detecting Local and Atmospheric Greenhouse Gases: A Review. SHILAP Revista de lepidopterología. 3(2). 11 indexed citations
5.
Yoshida, Yukio, Hirofumi Ohyama, Isamu Morino, et al.. (2023). Update on the GOSAT TANSO–FTS SWIR Level 2 retrieval algorithm. Atmospheric measurement techniques. 16(6). 1477–1501. 8 indexed citations
6.
Oh, Young‐Suk & Zhixiong Guo. (2023). APPLICABILITY OF MACHINE LEARNING TECHNIQUES IN PREDICTING SPECIFIC HEAT CAPACITY OF COMPLEX NANOFLUIDS. Heat Transfer Research. 55(3). 39–60. 7 indexed citations
7.
Park, Hyeri, Haklim Choi, Haeyoung Lee, et al.. (2023). Identifying emission sources of CH4 in East Asia based on in-situ observations of atmospheric δ13C-CH4 and C2H6. The Science of The Total Environment. 908. 168433–168433. 3 indexed citations
8.
Kim, Jae‐Min, Jinah Jang, Young‐Suk Oh, et al.. (2023). Anthropogenic carbon dioxide origin tracing study in Anmyeon-do, South Korea: Based on STILT-footprint and emissions data. The Science of The Total Environment. 894. 164677–164677. 2 indexed citations
9.
Lee, Haeyoung, Jinkyu Hong, Changsub Shim, et al.. (2022). Consideration of Top-down Greenhouse Gas Estimation Approaches to Prepare Carbon Neutral Policy. Journal of Korean Society for Atmospheric Environment. 38(6). 933–947. 2 indexed citations
10.
11.
Oh, Young‐Suk, Samuel Takele Kenea, Tae‐Young Goo, et al.. (2018). Characteristics of greenhouse gas concentrations derived from ground-based FTS spectra at Anmyeondo, South Korea. Atmospheric measurement techniques. 11(4). 2361–2374. 13 indexed citations
12.
Oh, Young‐Suk, et al.. (2001). The changes in expression of three subtypes of TTX sensitive sodium channels in sensory neurons after spinal nerve ligation. Molecular Brain Research. 95(1-2). 153–161. 139 indexed citations
13.
Oh, Young‐Suk & Stephen G. Waxman. (1998). Novel splice variants of the voltage-sensitive sodium channel alpha subunit. Neuroreport. 9(7). 1267–1272. 39 indexed citations
14.
Oh, Young‐Suk, Young Jae Lee, & Stephen G. Waxman. (1997). Regulation of Na+ channel β1 and β2 subunit mRNA levels in cultured rat astrocytes. Neuroscience Letters. 234(2-3). 107–110. 18 indexed citations
15.
Oh, Young‐Suk, et al.. (1995). Na+ channel β1 subunit mRNA: differential expression in rat spinal sensory neurons. Molecular Brain Research. 30(2). 357–361. 18 indexed citations
16.
17.
Oh, Young‐Suk, et al.. (1995). Na+ channel β1 subunit mRNA expression in developing rat central nervous system. Molecular Brain Research. 34(2). 239–250. 29 indexed citations
18.
Oh, Young‐Suk, Joel A. Black, & Stephen G. Waxman. (1994). The expression of rat brain voltage-sensitive Na+ channel mRNAs in astrocytes. Molecular Brain Research. 23(1-2). 57–65. 37 indexed citations
19.
Black, J.A., Shigeru Yokoyama, Stephen G. Waxman, et al.. (1994). Sodium channel mRNAs in cultured spinal cord astrocytes: in situ hybridization in identified cell types. Molecular Brain Research. 23(3). 235–245. 47 indexed citations
20.
Oh, Young‐Suk, Joel A. Black, & Stephen G. Waxman. (1994). Rat brain Na+ channel mRNAs in non‐excitable Schwann cells. FEBS Letters. 350(2-3). 342–346. 15 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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