Tzu‐Wei Wu

1.1k citations
24 papers · 809 · h-index 13

Impact in

Papers in

Tzu‐Wei Wu

22 papers receiving 795 citations

Peers

Tzu‐Wei Wu
Comparison fields: 5 of 89
  • Hepatology 140
  • Behavioral Neuroscience 39
  • Neurology 144
  • Endocrinology, Diabetes and Metabolism 129
  • Genetics 80
Replace F.J. Laso with:
F.J. Laso Spain
Masashiro Sugawara Japan
Susana Hernández Spain
A Bergk Germany
Dan Yang China
JuliaM. Polak United Kingdom
Damir Nizamutdinov United States
Mariana Postal Brazil
M Sobaniec‐Lotowska Poland
Tzu‐Wei Wu relative to F.J. Laso Spain F.J. Laso's profile →
Citations per field
00.5×5.7×
F.J. Laso · 1×
Citations per year

Countries citing papers authored by Tzu‐Wei Wu

Since Specialization
Citations

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

Fields of papers citing papers by Tzu‐Wei Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tzu‐Wei Wu. 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 Tzu‐Wei Wu. The network helps show where Tzu‐Wei Wu may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004195
2 2010166
3 1985121
4 201262
5 201151
6 201625
7 201323
8 201423
9 201422
10 199922
11 202218
12 201817
13 202117
14 200511
15 201910
16 20188
17 20186
18 20234
19 20233
20 20222

About Tzu‐Wei Wu

Tzu‐Wei Wu is a scholar working on Cardiology and Cardiovascular Medicine, Epidemiology, Molecular Biology, Endocrinology, Diabetes and Metabolism and Genetics, having authored 24 papers that have together received 809 indexed citations. Recurring topics across this work include Hepatitis B Virus Studies (5 papers), Cardiovascular Health and Disease Prevention (5 papers), Hepatitis C virus research (3 papers), Ion Transport and Channel Regulation (2 papers), Blood Pressure and Hypertension Studies (2 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (2 papers), Estrogen and related hormone effects (2 papers) and Cerebrovascular and Carotid Artery Diseases (2 papers). The work is most often cited by research in Hepatology (140 citations), Behavioral Neuroscience (39 citations), Neurology (144 citations), Endocrinology, Diabetes and Metabolism (129 citations) and Genetics (80 citations). Tzu‐Wei Wu has collaborated with scholars based in Taiwan, United States and United Kingdom. Frequent co-authors include Roberta Dı́az Brinton, Liqin Zhao, Liyu Wang, Hans Hsienhong Lin, Wei‐Ting Wang, Che-Kun James Shen, Kuen‐Jer Tsai, Shuhua Chen, Wei‐Lin Chien and Ching‐Po Lin. Their work appears in journals such as Hepatology, Brain Research, International Journal of Molecular Sciences, Journal of Atherosclerosis and Thrombosis and Scientific Reports.

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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