Jun Wu

5.8k citations
157 papers · 4.9k · h-index 38

Impact in

    • Electrospun Nanofibers in Biomedical Applications
  • Genetics top 2%
    • Mesenchymal stem cell research

Papers in

    • Sphingolipid Metabolism and Signaling 12
    • Glycosylation and Glycoproteins Research 8
    • Tissue Engineering and Regenerative Medicine 28

Jun Wu

153 papers receiving 4.7k citations

Peers

Jun Wu
Comparison fields: 5 of 142
  • Biomaterials 732
  • Genetics 339
  • Cellular and Molecular Neuroscience 583
  • Surgery 1.1k
  • Molecular Biology 1.8k
Replace Herman Yeger with:
Herman Yeger Canada
Shih‐Hwa Chiou Taiwan
Lucia Formigli Italy
Maya Simionescu Romania
Qian Li China
Liu Yang China
Angelo Corti Italy
Levon M. Khachigian Australia
Gordon Campbell Australia
Yong Wang China
Jun Wu relative to Herman Yeger Canada Herman Yeger's profile →
Citations per field
00.5×1.5×1.8×
Herman Yeger · 1×
Citations per year

Countries citing papers authored by Jun Wu

Since Specialization
Citations

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

Fields of papers citing papers by Jun Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 157 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2004270
2 2004264
3 2015234
4 1985214
5 1996176
6 2010143
7 2018136
8 2003128
9 2019119
10 2007118
11 1997115
12 1993105
13 199199
14 198590
15 200980
16 201676
17 202074
18 199471
19 202070
20 202070

About Jun Wu

Jun Wu is a scholar working on Molecular Biology, Surgery, Cardiology and Cardiovascular Medicine, Genetics and Biomaterials, having authored 157 papers that have together received 4.9k indexed citations. Recurring topics across this work include Tissue Engineering and Regenerative Medicine (28 papers), Mesenchymal stem cell research (18 papers), Electrospun Nanofibers in Biomedical Applications (15 papers), Sphingolipid Metabolism and Signaling (12 papers), Cardiac Fibrosis and Remodeling (11 papers), Cardiac Ischemia and Reperfusion (9 papers), Glycosylation and Glycoproteins Research (8 papers) and SARS-CoV-2 and COVID-19 Research (8 papers). The work is most often cited by research in Biomaterials (732 citations), Genetics (339 citations), Cellular and Molecular Neuroscience (583 citations), Surgery (1.1k citations) and Molecular Biology (1.8k citations). Jun Wu has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Ren‐Ke Li, Richard D. Weisel, Gregory M. Fahy, Brian Wowk, Tai-Wing Wu, Hsing‐Wen Sung, S.J. Paynter, Rui‐Dong Duan, J. Paul Bolam and John Powell. Their work appears in journals such as Life Sciences, Cell Transplantation, Biomaterials, Vaccines and Biochemistry and Cell Biology.

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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