Min Ai

767 citations
50 papers · 521 · h-index 13

Impact in

  • Biophysics top 5%
    • Spectroscopy Techniques in Biomedical and Chemical Research
    • Wound Healing and Treatments

Papers in

Min Ai

45 papers receiving 513 citations

Peers

Min Ai
Comparison fields: 5 of 113
  • Biophysics 73
  • Rehabilitation 41
  • Immunology and Allergy 34
  • Analytical Chemistry 43
  • Molecular Biology 212
Replace Xiang Shen with:
Xiang Shen China
Seyed Rouhollah Miri Iran
Veronika Vidová Czechia
Michael Warso United States
Yunbo Wei China
Jun Rao China
Stefan Eirefelt Sweden
Mary J. Schiedt United States
Yen-Chang Hsiao Taiwan
Xianyang Luo China
Min Ai relative to Xiang Shen China Xiang Shen's profile →
Citations per field
00.5×3.4×
Xiang Shen · 1×
Citations per year

Countries citing papers authored by Min Ai

Since Specialization
Citations

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

Fields of papers citing papers by Min Ai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1 200882
2 201747
3 202044
4 201934
5 201932
6 201931
7 201226
8 201621
9 200719
10 202117
11 202216
12 202212
13 202112
14 202011
15 201610
16 20208
17 20198
18 20197
19 20216
20 20246

About Min Ai

Min Ai is a scholar working on Molecular Biology, Surgery, Pathology and Forensic Medicine, Ecology, Evolution, Behavior and Systematics and Oncology, having authored 50 papers that have together received 521 indexed citations. Recurring topics across this work include Lichen and fungal ecology (5 papers), Mycorrhizal Fungi and Plant Interactions (5 papers), Liver Disease Diagnosis and Treatment (4 papers), Ubiquitin and proteasome pathways (4 papers), Protein Degradation and Inhibitors (3 papers), Plant Pathogens and Fungal Diseases (3 papers), Diabetic Foot Ulcer Assessment and Management (3 papers) and Wound Healing and Treatments (3 papers). The work is most often cited by research in Biophysics (73 citations), Rehabilitation (41 citations), Immunology and Allergy (34 citations), Analytical Chemistry (43 citations) and Molecular Biology (212 citations). Min Ai has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Lulu Ni, Liying Qiu, Guiwen Wang, Bao Hou, Huilu Yao, Yong-qing Li, Lixin Peng, Jiangnan Sun, Weiwei Cai and Yuetao Zhou. Their work appears in journals such as PLoS ONE, Journal of Medicinal Chemistry, European Journal of Medicinal Chemistry, Journal of Hepatology and International Journal of Molecular Sciences.

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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