Debin Wan

54 papers receiving 1.4k citations

Peers

Debin Wan
Comparison fields: 5 of 118
  • Biochemistry 343
  • Biological Psychiatry 34
  • Pharmacology 81
  • Molecular Biology 623
  • Pathology and Forensic Medicine 158
Replace Soumen K. Manna with:
Soumen K. Manna India
Marica Orioli Italy
Venkataraman Amarnath United States
Claire Vigor France
Dionicio Siegel United States
Thierry Oster France
И. Б. Заводник Belarus
Ross Shiman United States
Giovanna Baron Italy
Debin Wan relative to Soumen K. Manna India Soumen K. Manna's profile →
Citations per field
00.5×2.8×
Soumen K. Manna · 1×
Citations per year

Countries citing papers authored by Debin Wan

Since Specialization
Citations

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

Fields of papers citing papers by Debin Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 56 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020123
2 202084
3 201971
4 201865
5 202057
6 201950
7 201847
8 201642
9 202042
10 202040
11 201138
12 202238
13 201837
14 201236
15 201435
16 201234
17 201934
18 202033
19 202130
20 201927

About Debin Wan

Debin Wan is a scholar working on Biochemistry, Molecular Biology, Pathology and Forensic Medicine, Endocrinology, Diabetes and Metabolism and Radiology, Nuclear Medicine and Imaging, having authored 56 papers that have together received 1.4k indexed citations. Recurring topics across this work include Eicosanoids and Hypertension Pharmacology (27 papers), Alcohol Consumption and Health Effects (15 papers), Hormonal Regulation and Hypertension (8 papers), Monoclonal and Polyclonal Antibodies Research (7 papers), Analytical Chemistry and Chromatography (6 papers), Fatty Acid Research and Health (6 papers), Inflammatory mediators and NSAID effects (6 papers) and Mass Spectrometry Techniques and Applications (6 papers). The work is most often cited by research in Biochemistry (343 citations), Biological Psychiatry (34 citations), Pharmacology (81 citations), Molecular Biology (623 citations) and Pathology and Forensic Medicine (158 citations). Debin Wan has collaborated with scholars based in United States, China and Japan. Frequent co-authors include Bruce D. Hammock, Jun Yang, Sung Hee Hwang, Zhenfeng Li, Christophe Morisseau, Hongmei Yang, Natalia Vasylieva, Bogdan Barnych, Shuying Liu and Karen Wagner. Their work appears in journals such as Journal of Agricultural and Food Chemistry, Proceedings of the National Academy of Sciences, Analytical Chemistry, Scientific Reports and ACS Omega.

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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