Feiyue Sun

445 citations
19 papers · 318 · 1 hit paper · h-index 6

Impact in

Papers in

Feiyue Sun

17 papers receiving 314 citations

Feiyue Sun's Hit Papers

A deep learning method for predicting metabolite–disease associations via graph neural network 2022 · 202 citations
2020+1+2Years since publication50100150200

Peers

Feiyue Sun
Comparison fields: 5 of 75
  • Cancer Research 77
  • Safety, Risk, Reliability and Quality 46
  • Mechanics of Materials 89
  • Management, Monitoring, Policy and Law 32
  • Computational Theory and Mathematics 41
Replace Guan Ruan with:
Guan Ruan China
Linhan Zhang China
Yuan Zhi China
Lijun Deng China
Mohammad Hadigol United States
Yue An China
Sergio Oller Moreno Spain
Joonha Kwon South Korea
Patricia Gómez Spain
Emine Çelik United States
Feiyue Sun relative to Guan Ruan China Guan Ruan's profile →
Citations per field
00.5×10.3×
Guan Ruan · 1×
Citations per year

Countries citing papers authored by Feiyue Sun

Since Specialization
Citations

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

Fields of papers citing papers by Feiyue Sun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 24 scholars most cited alongside Feiyue Sun, 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 Feiyue Sun Line = papers co-authored together Feiyue Sun links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1
A deep learning method for predicting metabolite–disease associations via graph neural network
Hit paper breakdown →
2022202
2 202139
3 202319
4 202110
5 20227
6 20246
7 20225
8 20234
9 20234
10 20244
11 20224
12 20224
13 20233
14 20232
15 20242
16 20241
17 20211
18 20221
19 20230

About Feiyue Sun

Feiyue Sun is a scholar working on Mechanics of Materials, Management, Monitoring, Policy and Law, Civil and Structural Engineering, Safety, Risk, Reliability and Quality and Molecular Biology, having authored 19 papers that have together received 318 indexed citations. Recurring topics across this work include Rock Mechanics and Modeling (16 papers), Landslides and related hazards (11 papers), Geotechnical and Geomechanical Engineering (5 papers), Geomechanics and Mining Engineering (4 papers), Tunneling and Rock Mechanics (4 papers), Geophysical Methods and Applications (2 papers), Geotechnical Engineering and Analysis (2 papers) and Grouting, Rheology, and Soil Mechanics (2 papers). The work is most often cited by research in Cancer Research (77 citations), Safety, Risk, Reliability and Quality (46 citations), Mechanics of Materials (89 citations), Management, Monitoring, Policy and Law (32 citations) and Computational Theory and Mathematics (41 citations). Feiyue Sun has collaborated with scholars based in China. Frequent co-authors include Qi Zhao, Jianqiang Sun, Jiaqi Guo, Wenlong Wu, Xin Huang, Xiliang Liu, Zihui Zhu, Xiaoyan Shi, Hengyuan Zhang and Ben‐Guo He. Their work appears in journals such as Geotechnical and Geological Engineering, Bulletin of Engineering Geology and the Environment, Briefings in Bioinformatics, Applied Sciences and Buildings.

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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