Kuo‐Chen Chan

16 papers receiving 808 citations

Kuo‐Chen Chan's Hit Papers

Evinacumab for Homozygous Familial Hypercholesterolemia 2020 · 519 citations
5190+2+4Years since publication100200300400500

Peers

Kuo‐Chen Chan
Comparison fields: 5 of 53
  • Cardiology and Cardiovascular Medicine 461
  • Endocrinology, Diabetes and Metabolism 265
  • Cancer Research 162
  • Surgery 450
  • Immunology 57
Replace Nagwa Khilla with:
Nagwa Khilla United States
Shazia Ali United States
Adam Sukiennik Poland
C.D. Furberg United States
Joost Besseling Netherlands
Jonathan Fialkow United States
Alessia Di Costanzo Italy
E. Roeseler Germany
Curtis Rambaran United Kingdom
Johan G. Schnitzler Netherlands
Kuo‐Chen Chan relative to Nagwa Khilla United States Nagwa Khilla's profile →
Citations per field
00.5×3.4×
Nagwa Khilla · 1×
Citations per year

Countries citing papers authored by Kuo‐Chen Chan

Since Specialization
Citations

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

Fields of papers citing papers by Kuo‐Chen Chan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kuo‐Chen Chan. 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 Kuo‐Chen Chan. The network helps show where Kuo‐Chen Chan may publish in the future.

Co-authors

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

All Works

17 of 17 papers shown
#Work
1
Evinacumab for Homozygous Familial Hypercholesterolemia
Hit paper breakdown →
2020519
2 2019184
3 201970
4 201724
5 202318
6 20167
7 20166
8 20173
9 20232
10 20202
11 20172
12 20212
13 20202
14 20201
15 20201
16 20211
17 20230

About Kuo‐Chen Chan

Kuo‐Chen Chan is a scholar working on Surgery, Cardiology and Cardiovascular Medicine, Cancer Research, Endocrinology, Diabetes and Metabolism and Economics and Econometrics, having authored 17 papers that have together received 844 indexed citations. Recurring topics across this work include Lipoproteins and Cardiovascular Health (11 papers), Lipid metabolism and disorders (9 papers), Cancer, Lipids, and Metabolism (8 papers), Pharmaceutical Economics and Policy (1 paper), Atherosclerosis and Cardiovascular Diseases (1 paper) and Diabetes Treatment and Management (1 paper). The work is most often cited by research in Cardiology and Cardiovascular Medicine (461 citations), Endocrinology, Diabetes and Metabolism (265 citations), Cancer Research (162 citations), Surgery (450 citations) and Immunology (57 citations). Kuo‐Chen Chan has collaborated with scholars based in United States, Canada and Netherlands. Frequent co-authors include Robert Pordy, Daniel A. Gipe, Poulabi Banerjee, Daniel Gaudet, George D. Yancopoulos, Frederick J. Raal, Nagwa Khilla, Shazia Ali, John J.P. Kastelein and Paolo Rubba. Their work appears in journals such as Circulation, Journal of clinical lipidology, Atherosclerosis, Open Forum Infectious Diseases and European Heart Journal.

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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