Fei Wu

3.7k citations
72 papers · 2.1k indexed · 1 hit paper · h-index 15

Impact in

Papers in

Fei Wu

64 papers receiving 2.0k citations

Hit Papers

Gender Differences in Patients With COVID-19: Focus on Severity and Mortality 2020 · 1.4k citations
1.4k0+2+4Years since publication4008001.2k

Peers

Fei Wu
Comparison fields: 5 of 169
  • Infectious Diseases 942
  • Modeling and Simulation 213
  • Neurology 360
  • Obstetrics and Gynecology 168
  • Clinical Psychology 268
Replace Jiao Huang with:
Jiao Huang China
Huiying Liang China
Zhiguo Zhang China
Dou Li China
Zhaohui Tong China
Jinjun Ran China
Xiqian Wang China
Huifen Li United States
Kui Liu China
Tom Yates United Kingdom
Fei Wu relative to Jiao Huang China Jiao Huang's profile →
Citations per field
00.5×1.5×2.2×
Jiao Huang · 1×
Citations per year

Countries citing papers authored by Fei Wu

Since Specialization
Citations

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

Fields of papers citing papers by Fei Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 72 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Gender Differences in Patients With COVID-19: Focus on Severity and Mortality
Hit paper breakdown →
20201434
2 201558
3 200747
4 202230
5 202128
6 202328
7 202226
8 202125
9 202121
10 201421
11 202019
12 202219
13 202317
14
Discovery of Late Ordovician Subvolcanic Rocks in South China: Existence of Subduction-Related Dacite from Early Paleozoic?
201417
15 201315
16 202414
17 202113
18 202313
19 202213
20 202211

About Fei Wu

Fei Wu is a scholar working on Pollution, Health, Toxicology and Mutagenesis, Computer Vision and Pattern Recognition, Biomedical Engineering and Artificial Intelligence, having authored 72 papers that have together received 2.1k indexed citations. Recurring topics across this work include Heavy metals in environment (11 papers), Mercury impact and mitigation studies (11 papers), Toxic Organic Pollutants Impact (7 papers), Video Surveillance and Tracking Methods (5 papers), COVID-19 Clinical Research Studies (5 papers), Geochemistry and Geologic Mapping (5 papers), Caching and Content Delivery (4 papers) and Advanced Data Storage Technologies (4 papers). The work is most often cited by research in Infectious Diseases (942 citations), Modeling and Simulation (213 citations), Neurology (360 citations), Obstetrics and Gynecology (168 citations) and Clinical Psychology (268 citations). Fei Wu has collaborated with scholars based in China, United States and Germany. Frequent co-authors include Peng Bai, Wei He, Jin‐Kui Yang, Shi Liu, Jianmin Jin, De-min Han, Xiaofang Liu, Laura Simich, Xun Wang and Wei Yuan. Their work appears in journals such as Environmental Science & Technology, Nano Energy, Journal of Hazardous Materials, Scientific Reports and IEEE Transactions on Geoscience and Remote Sensing.

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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