Dehui Yin
About
In The Last Decade
Dehui Yin
2 papers receiving 532 citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Health Information Management 430
- Artificial Intelligence 317
- Molecular Biology 80
- Endocrinology, Diabetes and Metabolism 68
- Complementary and alternative medicine 64
Countries citing papers authored by Dehui Yin
This map shows the geographic impact of Dehui Yin'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 Dehui Yin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dehui Yin more than expected).
Fields of papers citing papers by Dehui Yin
This network shows the impact of papers produced by Dehui Yin. 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 Dehui Yin. The network helps show where Dehui Yin may publish in the future.
Co-authorship network of co-authors of Dehui Yin
This figure shows the co-authorship network connecting the top 25 collaborators of Dehui Yin. A scholar is included among the top collaborators of Dehui Yin based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dehui Yin. Dehui Yin is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | Predicting Diabetes Mellitus With Machine Learning Techniques breakdown → | 563 |
| 2 | 6 | |
| 3 | 1 |
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.