Ming Gan

1k citations
21 papers · 867 · h-index 13

Impact in

  • Neurology top 5%
    • Parkinson's Disease Mechanisms and Treatments
    • Neuroinflammation and Neurodegeneration Mechanisms
    • Barrier Structure and Function Studies
  • Physiology top 5%
    • Alzheimer's disease research and treatments

Papers in

Ming Gan

20 papers receiving 853 citations

Peers

Ming Gan
Comparison fields: 5 of 60
  • Neurology 232
  • Physiology 318
  • Neurology 73
  • Physiology 18
  • Cell Biology 52
Replace Eunju Im with:
Eunju Im South Korea
Christopher J. Holler United States
Michelle Rothaug Germany
Margaret Wong United States
Janet A. Brooks United States
Alzbeta Kralova Trancikova Slovakia
Veronica Ferrari Italy
Marc P. M. Soutar United Kingdom
Sara Grassi Italy
Michelle Gardner Germany
Ming Gan relative to Eunju Im South Korea Eunju Im's profile →
Citations per field
00.5×2×3×3.8×
Eunju Im · 1×
Citations per year

Countries citing papers authored by Ming Gan

Since Specialization
Citations

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

Fields of papers citing papers by Ming Gan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2012222
2 2016126
3 201099
4 201785
5 201280
6 201032
7 201432
8 201430
9 201627
10 201326
11 201426
12 201624
13 201317
14 200511
15 200510
16 20219
17 20035
18 20203
19 20031
20
Analysis of a survey on the infection of Achatina fulica with Angiostrongylus cantonensis in Guangzhou, Guangdong province.
20071

About Ming Gan

Ming Gan is a scholar working on Neurology, Otorhinolaryngology, Insect Science, Physiology and Public Health, Environmental and Occupational Health, having authored 21 papers that have together received 867 indexed citations. Recurring topics across this work include Parkinson's Disease Mechanisms and Treatments (10 papers), Alzheimer's disease research and treatments (3 papers), Head and Neck Cancer Studies (2 papers), Mosquito-borne diseases and control (2 papers), Insect symbiosis and bacterial influences (1 paper), Advanced Proteomics Techniques and Applications (1 paper), Mollusks and Parasites Studies (1 paper) and Neuroscience and Neuropharmacology Research (1 paper). The work is most often cited by research in Neurology (232 citations), Physiology (318 citations), Neurology (73 citations), Physiology (18 citations) and Cell Biology (52 citations). Ming Gan has collaborated with scholars based in United States and China. Frequent co-authors include Peizhou Jiang, Shu-Hui C. Yen, Dennis W. Dickson, Pamela J. McLean, Abdul Shukkur Ebrahim, Wen-Lang Lin, Takahisa Kanekiyo, Guojun Bu, Heather L. Melrose and Steve Estus. Their work appears in journals such as Acta Neuropathologica, Neurobiology of Aging, Frontiers in Aging Neuroscience, Insect Science and Frontiers in Molecular Neuroscience.

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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