Geng Shen

423 citations
32 papers · 277 · h-index 10

Impact in

Papers in

Geng Shen

28 papers receiving 274 citations

Peers

Geng Shen
Comparison fields: 5 of 66
  • Nutrition and Dietetics 43
  • Health, Toxicology and Mutagenesis 27
  • Physiology 32
  • Endocrinology, Diabetes and Metabolism 19
  • Oncology 30
Replace Tae Sasakabe with:
Tae Sasakabe Japan
Seiji Nakanishi Japan
Kaifa Tang China
Mohamed Tahar Sfar Tunisia
Qianlu Jin China
Satyendra Kumar Sonkar India
L. Zezza Italy
Beata Kasztelan‐Szczerbińska Poland
Xuewei Huang China
Conor‐James MacDonald France
Geng Shen relative to Tae Sasakabe Japan Tae Sasakabe's profile →
Citations per field
00.5×1.5×2.1×
Tae Sasakabe · 1×
Citations per year

Countries citing papers authored by Geng Shen

Since Specialization
Citations

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

Fields of papers citing papers by Geng Shen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202036
2 201930
3 201927
4 202418
5 202417
6 201717
7 201616
8 201914
9 201911
10 201610
11 20199
12 20238
13 20188
14 20208
15 20197
16 20197
17 20236
18 20206
19 20204
20 20243

About Geng Shen

Geng Shen is a scholar working on Cardiology and Cardiovascular Medicine, Molecular Biology, Oncology, Physiology and Health, Toxicology and Mutagenesis, having authored 32 papers that have together received 277 indexed citations. Recurring topics across this work include Inflammatory Biomarkers in Disease Prognosis (4 papers), Blood Pressure and Hypertension Studies (3 papers), Climate Change and Health Impacts (2 papers), Air Quality and Health Impacts (2 papers), Cardiovascular Disease and Adiposity (2 papers), Selenium in Biological Systems (2 papers), Telomeres, Telomerase, and Senescence (2 papers) and Cardiovascular Health and Risk Factors (2 papers). The work is most often cited by research in Nutrition and Dietetics (43 citations), Health, Toxicology and Mutagenesis (27 citations), Physiology (32 citations), Endocrinology, Diabetes and Metabolism (19 citations) and Oncology (30 citations). Geng Shen has collaborated with scholars based in China, United States and Nigeria. Frequent co-authors include Yuqing Huang, Yingqing Feng, Kenneth Lo, Chaolei Chen, Yu‐Ling Yu, Jiayi Huang, Lin Liu, Jie Li, Xin Su and Bin Zhang. Their work appears in journals such as Postgraduate Medical Journal, Frontiers in Cardiovascular Medicine, BMC Public Health, Journal of Biochemical and Molecular Toxicology and Journal of Hypertension.

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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