Suet Nee Chen

1.4k citations
29 papers · 1.1k · h-index 18

Impact in

Papers in

Suet Nee Chen

29 papers receiving 1.1k citations

Peers

Suet Nee Chen
Comparison fields: 5 of 103
  • Cardiology and Cardiovascular Medicine 516
  • Orthopedics and Sports Medicine 76
  • Cell Biology 115
  • Molecular Biology 455
  • Aging 7
Replace Jennifer Baum with:
Jennifer Baum United States
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Tillman Dahme Germany
Nobuhiko Shibata Japan
Hiroshi Sumida Japan
Takahiko Nishiyama Japan
Carla Ciccone United States
Peter K. Mays United Kingdom
Carmen Miano Italy
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Citations per field
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Citations per year

Countries citing papers authored by Suet Nee Chen

Since Specialization
Citations

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

Fields of papers citing papers by Suet Nee Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Suet Nee Chen. 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 Suet Nee Chen. The network helps show where Suet Nee Chen may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 29 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013221
2 2012113
3 2019110
4 2009101
5 201678
6 200266
7 202044
8 200944
9 201243
10 201833
11 200932
12 202227
13 202124
14 201922
15 202021
16 202020
17 202218
18 202017
19 20128
20 20206

About Suet Nee Chen

Suet Nee Chen is a scholar working on Cardiology and Cardiovascular Medicine, Molecular Biology, Cell Biology, Biomedical Engineering and Atomic and Molecular Physics, and Optics, having authored 29 papers that have together received 1.1k indexed citations. Recurring topics across this work include Cardiomyopathy and Myosin Studies (11 papers), Cardiovascular Effects of Exercise (8 papers), Nuclear Structure and Function (8 papers), Genomics and Chromatin Dynamics (3 papers), Cellular Mechanics and Interactions (3 papers), RNA Research and Splicing (3 papers), Cardiac electrophysiology and arrhythmias (2 papers) and Force Microscopy Techniques and Applications (2 papers). The work is most often cited by research in Cardiology and Cardiovascular Medicine (516 citations), Orthopedics and Sports Medicine (76 citations), Cell Biology (115 citations), Molecular Biology (455 citations) and Aging (7 citations). Suet Nee Chen has collaborated with scholars based in United States and Italy. Frequent co-authors include Raffaella Lombardi, Ali J. Marian, James T. Willerson, Priyatansh Gurha, Alessandra Ruggiero, Luisa Mestroni, Matthew R.G. Taylor, Grazyna Czernuszewicz, Haluk Beyenal and Zbigniew Lewandowski. Their work appears in journals such as International Journal of Molecular Sciences, Circulation, Circulation Research, Cardiovascular Research and BMC Cardiovascular Disorders.

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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