Sunwon Lee

1.2k citations
22 papers · 784 · 1 hit paper · h-index 10

Impact in

Papers in

    • Biomedical Text Mining and Ontologies 7
    • Bioinformatics and Genomic Networks 4
    • Gene expression and cancer classification 2
    • Topic Modeling 3
    • Sentiment Analysis and Opinion Mining 1

Sunwon Lee

22 papers receiving 772 citations

Sunwon Lee's Hit Papers

DSigDB: drug signatures database for gene set analysis 2015 · 435 citations
4350+3+7Years since publication100200300400

Peers

Sunwon Lee
Comparison fields: 5 of 90
  • Molecular Biology 342
  • Computational Theory and Mathematics 79
  • Cancer Research 58
  • Neurology 48
  • Biological Psychiatry 7
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Sunwon Lee relative to Kyubum Lee United States Kyubum Lee's profile →
Citations per field
00.5×1.5×
Kyubum Lee · 1×
Citations per year

Countries citing papers authored by Sunwon Lee

Since Specialization
Citations

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

Fields of papers citing papers by Sunwon Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 22 papers — load more, or switch the sort, to bring in the rest.

#Work
1
DSigDB: drug signatures database for gene set analysis
Hit paper breakdown →
2015435
2 201672
3 201044
4 201836
5 201633
6 201633
7 201532
8 201720
9 201615
10 201210
11 20129
12 20139
13 20167
14 20127
15 20186
16 20135
17 20124
18 20123
19 20181
20 20111

About Sunwon Lee

Sunwon Lee is a scholar working on Molecular Biology, Artificial Intelligence, Computational Theory and Mathematics, Neurology and Cardiology and Cardiovascular Medicine, having authored 22 papers that have together received 784 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (7 papers), Bioinformatics and Genomic Networks (4 papers), Topic Modeling (3 papers), Gene expression and cancer classification (2 papers), Computational Drug Discovery Methods (2 papers), Web Data Mining and Analysis (1 paper), Sentiment Analysis and Opinion Mining (1 paper) and Hepatitis B Virus Studies (1 paper). The work is most often cited by research in Molecular Biology (342 citations), Computational Theory and Mathematics (79 citations), Cancer Research (58 citations), Neurology (48 citations) and Biological Psychiatry (7 citations). Sunwon Lee has collaborated with scholars based in South Korea, United States and Puerto Rico. Frequent co-authors include Jaewoo Kang, Kyubum Lee, Aik Choon Tan, Minji Jeon, Jimin Shin, Minjae Yoo, Jihye Kim, Karen A. Ryall, Seongsoon Kim and Sunkyu Kim. Their work appears in journals such as PLoS ONE, Bioinformatics, BMC Medical Informatics and Decision Making, BMC Bioinformatics and Biology Direct.

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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