Kwang‐Ai Won

2.3k citations
7 papers · 1.7k indexed · 2 hit papers · h-index 6

Impact in

  • Oncology top 5%
    • Cancer-related Molecular Pathways
    • Microtubule and mitosis dynamics

Papers in

    • Cancer-related Molecular Pathways 2
    • Peptidase Inhibition and Analysis 1

Kwang‐Ai Won

7 papers receiving 1.7k citations

Hit Papers

Triple‑negative breast cancer therapy: Current and future perspectives (Review) 2020 · 333 citations
3331999202620082017100200300400500

Peers

Kwang‐Ai Won
Comparison fields: 5 of 91
  • Oncology 890
  • Cell Biology 384
  • Molecular Biology 1.2k
  • Cancer Research 257
  • Hepatology 126
Replace Constadina Arvanitis with:
Constadina Arvanitis United States
David Leggett United States
Yukinori Minoshima Japan
Carolyn Sidor United States
JEFF EVANS United Kingdom
Katherine G. Moss United States
Tijana Borovski Netherlands
Lo‐Kong Chan Hong Kong
Omar Kabbarah United States
Chee Wee Ong Singapore
Kwang‐Ai Won relative to Constadina Arvanitis United States Constadina Arvanitis's profile →
Citations per field
00.5×1.5×1.8×
Constadina Arvanitis · 1×
Citations per year

Countries citing papers authored by Kwang‐Ai Won

Since Specialization
Citations

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

Fields of papers citing papers by Kwang‐Ai Won

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kwang‐Ai Won. 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 Kwang‐Ai Won. The network helps show where Kwang‐Ai Won may publish in the future.

Co-authorship network

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

All Works

7 of 7 papers shown
#Work
1
Triple‑negative breast cancer therapy: Current and future perspectives (Review)
Hit paper breakdown →
2020333
2 20168
3 2009282
4 2001491
5
Deregulated cyclin E induces chromosome instability
Hit paper breakdown →
1999564
6 19915
7 199012

About Kwang‐Ai Won

Kwang‐Ai Won is a scholar working on Oncology, Hepatology, Cell Biology, Immunology and Hematology, having authored 7 papers that have together received 1.7k indexed citations. Recurring topics across this work include DNA Repair Mechanisms (2 papers), Immune Response and Inflammation (2 papers), Cancer-related Molecular Pathways (2 papers), Microtubule and mitosis dynamics (2 papers), Advanced Breast Cancer Therapies (1 paper), Cancer Mechanisms and Therapy (1 paper), Peptidase Inhibition and Analysis (1 paper) and Ubiquitin and proteasome pathways (1 paper). The work is most often cited by research in Oncology (890 citations), Cell Biology (384 citations), Molecular Biology (1.2k citations), Cancer Research (257 citations) and Hepatology (126 citations). Kwang‐Ai Won has collaborated with scholars based in United States, Sweden and South Korea. Frequent co-authors include Charles Spruck, Steven I. Reed, Heimo Strohmaier, Peter Kaiser, Olle Sangfelt, Fawn Qian, F. Michael Yakes, Toshihiro Yamaguchi, Julie C. Lougheed and Alison Joly. Their work appears in journals such as Nature, Molecular and Cellular Biology, International Journal of Oncology, Cancer Research and BMC Cancer.

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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