Weisi Fu

610 citations
13 papers · 484 · h-index 12

Impact in

  • Physiology top 10%
    • Pain Mechanisms and Treatments
  • Neurology top 10%
    • Amyotrophic Lateral Sclerosis Research
    • Parkinson's Disease Mechanisms and Treatments

Papers in

Weisi Fu

13 papers receiving 481 citations

Peers

Weisi Fu
Comparison fields: 5 of 85
  • Physiology 189
  • Neurology 105
  • Sensory Systems 35
  • Cellular and Molecular Neuroscience 134
  • Cell Biology 75
Replace Masaki Tanaka with:
Masaki Tanaka Japan
Diego E. Hernández Chile
Sangwoo Ham South Korea
Christine V. Möser Germany
Maria L. Florez‐McClure United States
Kristine Y. Wang United States
Enji Zhang South Korea
Khurshed A. Katki United States
Laura Texidó Spain
Guofeng Lou China
Weisi Fu relative to Masaki Tanaka Japan Masaki Tanaka's profile →
Citations per field
00.5×2.8×
Masaki Tanaka · 1×
Citations per year

Countries citing papers authored by Weisi Fu

Since Specialization
Citations

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

Fields of papers citing papers by Weisi Fu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

13 of 13 papers shown
#Work
1 2009157
2 201052
3 201942
4 201339
5 201934
6 201929
7 201727
8 201923
9 201523
10 201417
11 202016
12 201914
13
Differential fatty acid profile in adipose and non-adipose tissues in obese mice.
201011

About Weisi Fu

Weisi Fu is a scholar working on Physiology, Cellular and Molecular Neuroscience, Sensory Systems, Surgery and Molecular Biology, having authored 13 papers that have together received 484 indexed citations. Recurring topics across this work include Pain Mechanisms and Treatments (8 papers), Ion Channels and Receptors (4 papers), Neuropeptides and Animal Physiology (3 papers), Biochemical effects in animals (2 papers), Anesthesia and Pain Management (2 papers), Diverse Interdisciplinary Research Innovations (1 paper), Amyotrophic Lateral Sclerosis Research (1 paper) and Fatty Acid Research and Health (1 paper). The work is most often cited by research in Physiology (189 citations), Neurology (105 citations), Sensory Systems (35 citations), Cellular and Molecular Neuroscience (134 citations) and Cell Biology (75 citations). Weisi Fu has collaborated with scholars based in United States, China and Sweden. Frequent co-authors include Bradley K. Taylor, Marie W. Wooten, Ping Shi, Renée R. Donahue, Jiayu Zhang, David M. Kwinter, Anna‐Lena Ström, József Gál, Haining Zhu and Gregory Corder. Their work appears in journals such as Neuropharmacology, Journal of Neurochemistry, Frontiers in Neuroscience, Neuroscience and Pain.

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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