Geming Shi

465 citations
26 papers · 385 · h-index 14

Impact in

Papers in

Geming Shi

25 papers receiving 384 citations

Peers

Geming Shi
Comparison fields: 5 of 82
  • Biological Psychiatry 35
  • Behavioral Neuroscience 48
  • Cellular and Molecular Neuroscience 78
  • Endocrinology, Diabetes and Metabolism 64
  • Aging 6
Replace Yunxiao Kang with:
Yunxiao Kang China
Narumi Hashikawa‐Hobara Japan
Sarah K. Woody United States
Akiko Ohashi Japan
Imane Lejri Switzerland
Ching‐Tien Lee Taiwan
William Nguyen United States
Mengyang Feng United States
Cuige Shi China
Mingwei Wang China
Geming Shi relative to Yunxiao Kang China Yunxiao Kang's profile →
Citations per field
00.5×1.5×
Yunxiao Kang · 1×
Citations per year

Countries citing papers authored by Geming Shi

Since Specialization
Citations

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

Fields of papers citing papers by Geming Shi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201743
2 201827
3 201325
4 201124
5 201721
6 202120
7 202020
8 201619
9 201119
10 201918
11 201717
12 202017
13 202114
14 201613
15 202312
16 201712
17 201711
18 202411
19 201510
20 20159

About Geming Shi

Geming Shi is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience, Biological Psychiatry, Behavioral Neuroscience and Physiology, having authored 26 papers that have together received 385 indexed citations. Recurring topics across this work include Tryptophan and brain disorders (8 papers), Stress Responses and Cortisol (8 papers), Hormonal and reproductive studies (6 papers), Neurotransmitter Receptor Influence on Behavior (6 papers), Genomics, phytochemicals, and oxidative stress (5 papers), Mitochondrial Function and Pathology (4 papers), Adipose Tissue and Metabolism (3 papers) and Neuroinflammation and Neurodegeneration Mechanisms (3 papers). The work is most often cited by research in Biological Psychiatry (35 citations), Behavioral Neuroscience (48 citations), Cellular and Molecular Neuroscience (78 citations), Endocrinology, Diabetes and Metabolism (64 citations) and Aging (6 citations). Geming Shi has collaborated with scholars based in China, Canada and Bulgaria. Frequent co-authors include Yunxiao Kang, Guoliang Zhang, Xiaoming Ji, Rui Cui, Huixian Cui, Shuangcheng Li, Huibing Tan, Yingkun Li, Yu Wang and Huixian Cui. Their work appears in journals such as Aging, Oxidative Medicine and Cellular Longevity, Scientific Reports, Neuropharmacology and Frontiers in Aging 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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