Su‐Young Kim

191 papers receiving 5.0k citations

Peers

Su‐Young Kim
Comparison fields: 5 of 158
  • Microbiology 93
  • Small Animals 855
  • Infectious Diseases 1.5k
  • Epidemiology 2.2k
  • Immunology 608
Replace Tomoko Betsuyaku with:
Tomoko Betsuyaku Japan
Edward D. Chan United States
Shoji Kudoh Japan
Fumio Nomura Japan
Eitan Kerem Israel
Young‐Jae Cho South Korea
Ken Ohta Japan
Daniel Berg United States
Yoshinori Hasegawa Japan
James R. Yankaskas United States
Su‐Young Kim relative to Tomoko Betsuyaku Japan Tomoko Betsuyaku's profile →
Citations per field
00.5×10×
Tomoko Betsuyaku · 1×
Citations per year

Countries citing papers authored by Su‐Young Kim

Since Specialization
Citations

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

Fields of papers citing papers by Su‐Young Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Su‐Young Kim, 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 Su‐Young Kim Line = papers co-authored together Su‐Young Kim links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20250
2 20241
3 20245
4 202412
5 20234
6 20236
7 20236
8 20212
9 201849
10 201785
11
An Exploration of Nonlinear Latent Growth Model Using Exponential Function: As an Alternative to Quadratic LGM
20170
12 201741
13 201367
14 201132
15 200935
16
Noninfectious Severe Early Chamber Reaction after Penetrating Keratoplasty
20073
17 2004137
18
Comparative study between proportional method and directional method in locating acupoints at forearm
20042
19
Effects of Bee Venom on the pain, edema, and acute inflammatory reactant of Rheumatoid Arthritis patients
200312
20
On a fuzzy detection scheme for weak stochastic signals
19921

About Su‐Young Kim

Su‐Young Kim is a scholar working on Small Animals, Microbiology, Infectious Diseases, Epidemiology and Ophthalmology, having authored 203 papers that have together received 5.1k indexed citations. Recurring topics across this work include Mycobacterium research and diagnosis (87 papers), Tuberculosis Research and Epidemiology (73 papers), Infectious Diseases and Mycology (40 papers), Intraocular Surgery and Lenses (10 papers), Quinazolinone synthesis and applications (10 papers), Glaucoma and retinal disorders (8 papers), Corneal surgery and disorders (6 papers) and Quantum Dots Synthesis And Properties (5 papers). The work is most often cited by research in Microbiology (93 citations), Small Animals (855 citations), Infectious Diseases (1.5k citations), Epidemiology (2.2k citations) and Immunology (608 citations). Su‐Young Kim has collaborated with scholars based in South Korea, United States and Japan. Frequent co-authors include Sung Jae Shin, Won‐Jung Koh, Kyeongman Jeon, Hee Jae Huh, Nam Yong Lee, Chang‐Seok Ki, Byung Woo Jhun, Hye Yun Park, Charles L. Daley and Byeong‐Ho Jeong. Their work appears in journals such as Antimicrobial Agents and Chemotherapy, Scientific Reports, Diagnostic Microbiology and Infectious Disease, The International Journal of Tuberculosis and Lung Disease and Journal of Clinical Microbiology.

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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