Won Gu Kim

9.0k citations
228 papers · 5.8k · h-index 45

Impact in

Papers in

Won Gu Kim

216 papers receiving 5.7k citations

Peers

Won Gu Kim
Comparison fields: 5 of 114
  • Endocrinology, Diabetes and Metabolism 4.1k
  • Pathology and Forensic Medicine 544
  • Surgery 1.2k
  • Oncology 714
  • Anatomy 36
Replace Clara Ugolini with:
Clara Ugolini Italy
Eleonora Molinaro Italy
Laura Agate Italy
Kiminori Sugino Japan
Takashi Uruno Japan
Dong Eun Song South Korea
Chisato Tomoda Japan
Min Ji Jeon South Korea
Shiro Noguchi Japan
Yuuki Takamura Japan
Won Gu Kim relative to Clara Ugolini Italy Clara Ugolini's profile →
Citations per field
00.5×1.5×2.0×
Clara Ugolini · 1×
Citations per year

Countries citing papers authored by Won Gu Kim

Since Specialization
Citations

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

Fields of papers citing papers by Won Gu Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Won Gu 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 Won Gu Kim. The network helps show where Won Gu Kim may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 228 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008176
2 2017151
3 2009146
4 2018140
5 2017101
6 201398
7 201595
8 201292
9 201190
10 201988
11 201784
12 201782
13 201782
14 200881
15 201776
16 201473
17 201472
18 201371
19 201367
20 201665

About Won Gu Kim

Won Gu Kim is a scholar working on Endocrinology, Diabetes and Metabolism, Surgery, Oncology, Molecular Biology and Pathology and Forensic Medicine, having authored 228 papers that have together received 5.8k indexed citations. Recurring topics across this work include Thyroid Cancer Diagnosis and Treatment (145 papers), Thyroid Disorders and Treatments (17 papers), Thyroid and Parathyroid Surgery (13 papers), Lymphoma Diagnosis and Treatment (11 papers), Cancer, Hypoxia, and Metabolism (8 papers), Head and Neck Anomalies (7 papers), BRCA gene mutations in cancer (6 papers) and Cancer-related Molecular Pathways (6 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (4.1k citations), Pathology and Forensic Medicine (544 citations), Surgery (1.2k citations), Oncology (714 citations) and Anatomy (36 citations). Won Gu Kim has collaborated with scholars based in South Korea, United States and Japan. Frequent co-authors include Tae Yong Kim, Young Kee Shong, Won Bae Kim, Min Ji Jeon, Suck Joon Hong, Dong Eun Song, Hyemi Kwon, Mijin Kim, Hye‐Seon Oh and Tae‐Yon Sung. Their work appears in journals such as Thyroid, Clinical Endocrinology, The Journal of Clinical Endocrinology & Metabolism, European Journal of Endocrinology and Annals of Surgical Oncology.

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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