Won Don Lee

920 citations
56 papers · 725 · h-index 16

Impact in

Papers in

Won Don Lee

49 papers receiving 686 citations

Peers

Won Don Lee
Comparison fields: 5 of 93
  • Reproductive Medicine 362
  • Public Health, Environmental and Occupational Health 370
  • Pediatrics, Perinatology and Child Health 87
  • Immunology 43
  • Obstetrics and Gynecology 14
Replace H. Nadir Çıray with:
H. Nadir Çıray Türkiye
Cristina Hickman United Kingdom
Tsung-Chieh Jackson Wu United States
Carol Lynn Curchoe United States
Jonas Malmsten United States
Zhongyuan Yao China
Ling Sun China
I‐Chung Chen Taiwan
Mojgan Akbarzadeh‐Jahromi Iran
Pantelis Zisimopoulos United States
Won Don Lee relative to H. Nadir Çıray Türkiye H. Nadir Çıray's profile →
Citations per field
00.5×1.5×
H. Nadir Çıray · 1×
Citations per year

Countries citing papers authored by Won Don Lee

Since Specialization
Citations

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

Fields of papers citing papers by Won Don Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200697
2 200693
3 200644
4 201232
5 201328
6 201328
7 201328
8 201128
9 200927
10 201623
11 201422
12 200621
13 201320
14 200619
15 201717
16 200916
17 200815
18 200814
19 201214
20 200713

About Won Don Lee

Won Don Lee is a scholar working on Reproductive Medicine, Artificial Intelligence, Public Health, Environmental and Occupational Health, Computer Vision and Pattern Recognition and Molecular Biology, having authored 56 papers that have together received 725 indexed citations. Recurring topics across this work include Ovarian function and disorders (14 papers), Reproductive Biology and Fertility (13 papers), Neural Networks and Applications (7 papers), Data Mining Algorithms and Applications (5 papers), Assisted Reproductive Technology and Twin Pregnancy (5 papers), Sperm and Testicular Function (3 papers), Face and Expression Recognition (3 papers) and Machine Learning and Data Classification (3 papers). The work is most often cited by research in Reproductive Medicine (362 citations), Public Health, Environmental and Occupational Health (370 citations), Pediatrics, Perinatology and Child Health (87 citations), Immunology (43 citations) and Obstetrics and Gynecology (14 citations). Won Don Lee has collaborated with scholars based in South Korea, Puerto Rico and Ethiopia. Frequent co-authors include Jin Ho Lim, Seok Hyun Kim, Byung Chul Jee, Chang Suk Suh, Ki Chul Kim, Seung‐Yup Ku, Eun‐Young Kim, Jin Tae, Nam Hyung Kim and Chihwa Song. Their work appears in journals such as Fertility and Sterility, Journal of Assisted Reproduction and Genetics, Daehan saengsik uihak hoeji/Clinical and experimental reproductive medicine, ETRI Journal and Acta Obstetricia Et Gynecologica Scandinavica.

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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