Miduo Tan

936 citations
23 papers · 692 · h-index 16

Impact in

Papers in

    • Circular RNAs in diseases 2
    • Molecular Biology Techniques and Applications 2
    • Epigenetics and DNA Methylation 2
    • Autophagy in Disease and Therapy 7

Miduo Tan

21 papers receiving 687 citations

Peers

Miduo Tan
Comparison fields: 5 of 92
  • Geriatrics and Gerontology 31
  • Cancer Research 136
  • Health, Toxicology and Mutagenesis 70
  • Molecular Biology 343
  • Epidemiology 136
Replace Shuya Kasai with:
Shuya Kasai Japan
Sreejayan Nair United States
Xiaohong Chen China
Zhenhua Ni China
Guanghai Yan China
Emilie Dubois‐Deruy France
Honglei Ji China
Hong‐Tai Chang Taiwan
Pelin Telkoparan‐Akillilar Türkiye
Lakhan Kma India
Miduo Tan relative to Shuya Kasai Japan Shuya Kasai's profile →
Citations per field
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Shuya Kasai · 1×
Citations per year

Countries citing papers authored by Miduo Tan

Since Specialization
Citations

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

Fields of papers citing papers by Miduo Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020153
2 201772
3 201659
4 201552
5 202041
6 201840
7 202137
8 201932
9 202128
10 201727
11 202225
12 201821
13 202120
14 202320
15 202216
16 202315
17 202012
18 20228
19 20205
20
Construction and validation of an eight pyroptosis-related lncRNA risk model for breast cancer.
20225

About Miduo Tan

Miduo Tan is a scholar working on Molecular Biology, Epidemiology, Health, Toxicology and Mutagenesis, Cancer Research and Biomedical Engineering, having authored 23 papers that have together received 692 indexed citations. Recurring topics across this work include Autophagy in Disease and Therapy (7 papers), MicroRNA in disease regulation (3 papers), Heavy Metal Exposure and Toxicity (3 papers), Circular RNAs in diseases (2 papers), Molecular Biology Techniques and Applications (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Epigenetics and DNA Methylation (2 papers) and AI in cancer detection (2 papers). The work is most often cited by research in Geriatrics and Gerontology (31 citations), Cancer Research (136 citations), Health, Toxicology and Mutagenesis (70 citations), Molecular Biology (343 citations) and Epidemiology (136 citations). Miduo Tan has collaborated with scholars based in China, United States and Tanzania. Frequent co-authors include Yuan Liu, Zhu Chen, Elingarami Sauli, Yan Deng, Wen Li, Nongyue He, Huifeng Pi, Ziyu He, Taotao Li and Li Song. Their work appears in journals such as Free Radical Research, Ecotoxicology and Environmental Safety, Environment International, Cell Proliferation and Biochemical and Biophysical Research Communications.

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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