Minshi Chen

463 citations
16 papers · 364 · h-index 8

Impact in

Papers in

Minshi Chen

16 papers receiving 359 citations

Peers

Minshi Chen
Comparison fields: 5 of 76
  • Industrial and Manufacturing Engineering 153
  • Health, Toxicology and Mutagenesis 39
  • Analytical Chemistry 23
  • Computational Theory and Mathematics 32
  • Management Information Systems 13
Replace Melis Onel with:
Melis Onel United States
Markus Schmitz Germany
Scott D. Barnicki United States
Hiroya Seki Japan
Jianhua Cao China
Noureddin Sadawi United Kingdom
Dean V. Neubauer United States
Pooja Bhalode United States
Mouloud Amazouz Canada
Minshi Chen relative to Melis Onel United States Melis Onel's profile →
Citations per field
00.5×7.0×
Melis Onel · 1×
Citations per year

Countries citing papers authored by Minshi Chen

Since Specialization
Citations

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

Fields of papers citing papers by Minshi Chen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

16 of 16 papers shown
#Work
1 202090
2 201871
3 202156
4 202048
5 201331
6 201315
7 201914
8 201913
9 20116
10 20215
11 20234
12 20194
13 20204
14 20121
15 20221
16 20191

About Minshi Chen

Minshi Chen is a scholar working on Molecular Biology, Biomedical Engineering, Computer Vision and Pattern Recognition, Health, Toxicology and Mutagenesis and Artificial Intelligence, having authored 16 papers that have together received 364 indexed citations. Recurring topics across this work include Biosensors and Analytical Detection (4 papers), Advanced biosensing and bioanalysis techniques (3 papers), Multimodal Machine Learning Applications (2 papers), Advanced Manufacturing and Logistics Optimization (2 papers), Digital Transformation in Industry (2 papers), Advanced Neural Network Applications (2 papers), Effects and risks of endocrine disrupting chemicals (2 papers) and Melamine detection and toxicity (2 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (153 citations), Health, Toxicology and Mutagenesis (39 citations), Analytical Chemistry (23 citations), Computational Theory and Mathematics (32 citations) and Management Information Systems (13 citations). Minshi Chen has collaborated with scholars based in China, Taiwan and Lebanon. Frequent co-authors include Hongfei Guo, Ting Qu, Jianke Li, Mohamed Khalgui, Gary G. Yen, Kai Zhang, Xin Xu, Weipeng Liu, Aori Qileng and Yu Zhang. Their work appears in journals such as Chinese Journal of Chemistry, Shock and Vibration, The International Journal of Advanced Manufacturing Technology, Applied Soft Computing and Biosensors and Bioelectronics.

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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