Kevin Song

3.2k citations
18 papers · 2.2k indexed · 1 hit paper · h-index 9

Kevin Song

17 papers receiving 2.2k citations

Hit Papers

CHARMM-GUIMembrane Buildertoward realistic biological mem...1.8k201420262018202250010001.5k

Peers

Kevin Song
Comparison fields: 5 of 141
  • Molecular Biology 1.7k
  • Microbiology 123
  • Cellular and Molecular Neuroscience 331
  • Sensory Systems 49
  • Spectroscopy 148
Replace Viviana Monje‐Galvan with:
Viviana Monje‐Galvan United States
Emilia L. Wu United States
Joseph B. Lim United States
Huan Rui United States
Ulrich Zachariae United Kingdom
Tao Yuan China
Clément Arnarez Netherlands
Gurpreet Singh India
Lu‐Yun Lian United Kingdom
Dhilon S. Patel United States
Kevin Song relative to Viviana Monje‐Galvan United States Viviana Monje‐Galvan's profile →
Citations per field
00.5×1.5×
Viviana Monje‐Galvan · 1×
Citations per year

Countries citing papers authored by Kevin Song

Since Specialization
Citations

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

Fields of papers citing papers by Kevin Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Kevin Song, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Kevin Song Line = papers co-authored together Kevin Song links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 20241
2 20233
3 20235
4 202222
5 20216
6 202111
7 20150
8
CHARMM-GUIMembrane Buildertoward realistic biological membrane simulationsbreakdown →
20141836
9 201435
10 201310
11 20122
12 20111
13 2011172
14 20114
15 201153
16 201128
17 20114
18 200324

About Kevin Song

Kevin Song is a scholar working on Biophysics, Sensory Systems and Molecular Biology, having authored 18 papers that have together received 2.2k indexed citations. Recurring topics across this work include Lipid Membrane Structure and Behavior (3 papers), Glycosylation and Glycoproteins Research (2 papers), Spectroscopy and Quantum Chemical Studies (2 papers), Extracellular vesicles in disease (2 papers), Protein Structure and Dynamics (2 papers), Electron Spin Resonance Studies (2 papers), Planarian Biology and Electrostimulation (2 papers) and MicroRNA in disease regulation (2 papers). The work is most often cited by research in Molecular Biology (1.7k citations), Microbiology (123 citations) and Cellular and Molecular Neuroscience (331 citations). Kevin Song has collaborated with scholars based in United States, Sweden and Canada. Frequent co-authors include Wonpil Im, Sunhwan Jo, Jeffery B. Klauda, Yifei Qi, Emilia L. Wu, Richard M. Venable, Jumin Lee, Viviana Monje‐Galvan, Huan Rui and Xi Cheng. Their work appears in journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and The Journal of Physical Chemistry B.

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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