Ming Ge

21 papers receiving 213 citations

Peers

Ming Ge
Comparison fields: 5 of 77
  • Neurology 30
  • Cellular and Molecular Neuroscience 65
  • Sensory Systems 15
  • Industrial and Manufacturing Engineering 23
  • Cognitive Neuroscience 36
Replace Ziyue Wang with:
Ziyue Wang China
Han‐Eol Park South Korea
Sergio Castaño-Castaño Spain
Inga Wang United States
Michael Mamoun United States
E. Pfister Germany
Weizhuo Li China
Ronen Shechter United States
Anusha Gandhi United States
Bo-Heng Liu China
Ming Ge relative to Ziyue Wang China Ziyue Wang's profile →
Citations per field
00.5×7.7×
Ziyue Wang · 1×
Citations per year

Countries citing papers authored by Ming Ge

Since Specialization
Citations

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

Fields of papers citing papers by Ming Ge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 25 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201046
2 201037
3 201125
4 202217
5 202315
6 202413
7 200911
8 202210
9 202010
10 20248
11 20237
12 20254
13 20132
14 20242
15 20232
16 19912
17 20152
18 20221
19 20171
20 19891

About Ming Ge

Ming Ge is a scholar working on Cellular and Molecular Neuroscience, Mechanical Engineering, Molecular Biology, Control and Systems Engineering and Computer Vision and Pattern Recognition, having authored 25 papers that have together received 217 indexed citations. Recurring topics across this work include Advanced Photocatalysis Techniques (2 papers), Neuroscience and Neuropharmacology Research (2 papers), Genomics, phytochemicals, and oxidative stress (2 papers), Robotic Path Planning Algorithms (2 papers), Gas Sensing Nanomaterials and Sensors (2 papers), Cerebrospinal fluid and hydrocephalus (1 paper), Ferroptosis and cancer prognosis (1 paper) and Infrastructure Maintenance and Monitoring (1 paper). The work is most often cited by research in Neurology (30 citations), Cellular and Molecular Neuroscience (65 citations), Sensory Systems (15 citations), Industrial and Manufacturing Engineering (23 citations) and Cognitive Neuroscience (36 citations). Ming Ge has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Jinhui Wang, Li Huang, Sudong Guan, Yan Zhu, Na Chen, Genke Yang, Fengyu Zhang, Yan Zhu, Na Chen and Dongbo Liu. Their work appears in journals such as Biochemical and Biophysical Research Communications, Ecotoxicology and Environmental Safety, Computers & Operations Research, Chemico-Biological Interactions and IEEE Transactions on Control Systems Technology.

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.

Explore authors with similar magnitude of impact