Danh-Tai Hoang
Impact in
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- Theoretical and Computational Physics
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- Diabetes Management and Research
Papers in
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- Theoretical and Computational Physics 5
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- Neural dynamics and brain function 5
- Co-authors
- Junghyo Jo (9 shared papers)Vipul Periwal (5 shared papers)Manami Hara (2 shared papers)H. T. Diep (6 shared papers)Junghyo Jo (1 shared paper)Masaki Nagaya (1 shared paper)J. Michael Millis (1 shared paper)Hiroshi Nagashima (1 shared paper)
- Journals
- Journal of Clinical Oncology (4 papers)Physical review. E (3 papers)Journal of Physics Condensed Matter (2 papers)PLoS ONE (2 papers)Cancer Research (2 papers)
- Partner nations
- United StatesSouth KoreaVietnam
In The Last Decade
Danh-Tai Hoang
21 papers receiving 148 citations
Peers
Comparison fields: 5 of 53
- Condensed Matter Physics 24
- Endocrinology, Diabetes and Metabolism 29
- Health Informatics 2
- Surgery 55
- Statistical and Nonlinear Physics 14
Countries citing papers authored by Danh-Tai Hoang
This map shows the geographic impact of Danh-Tai Hoang'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 Danh-Tai Hoang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danh-Tai Hoang more than expected).
Fields of papers citing papers by Danh-Tai Hoang
This network shows the impact of papers produced by Danh-Tai Hoang. 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 Danh-Tai Hoang. The network helps show where Danh-Tai Hoang may publish in the future.
Co-authors
The 25 scholars most cited alongside Danh-Tai Hoang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 41 | |
| 2 | 2024 | 28 | |
| 3 | 2016 | 17 | |
| 4 | 2019 | 10 | |
| 5 | 2019 | 10 | |
| 6 | 2012 | 9 | |
| 7 | 2013 | 6 | |
| 8 | 2016 | 4 | |
| 9 | 2013 | 4 | |
| 10 | 2023 | 3 | |
| 11 | 2020 | 3 | |
| 12 | 2017 | 3 | |
| 13 | Maximizing weighted Shannon entropy for network inference with little data | 2017 | 2 |
| 14 | 2025 | 2 | |
| 15 | 2013 | 2 | |
| 16 | 2012 | 2 | |
| 17 | 2022 | 1 | |
| 18 | 2022 | 1 | |
| 19 | 2022 | 1 | |
| 20 | 2013 | 1 |
About Danh-Tai Hoang
Danh-Tai Hoang is a scholar working on Condensed Matter Physics, Cognitive Neuroscience, Artificial Intelligence, Surgery and Molecular Biology, having authored 23 papers that have together received 151 indexed citations. Recurring topics across this work include Theoretical and Computational Physics (5 papers), Neural dynamics and brain function (5 papers), Pancreatic function and diabetes (4 papers), Diabetes Management and Research (3 papers), Diabetes and associated disorders (3 papers), Radiomics and Machine Learning in Medical Imaging (3 papers), Magnetic properties of thin films (3 papers) and AI in cancer detection (3 papers). The work is most often cited by research in Condensed Matter Physics (24 citations), Endocrinology, Diabetes and Metabolism (29 citations), Health Informatics (2 citations), Surgery (55 citations) and Statistical and Nonlinear Physics (14 citations). Danh-Tai Hoang has collaborated with scholars based in United States, South Korea and Vietnam. Frequent co-authors include Junghyo Jo, Vipul Periwal, Manami Hara, H. T. Diep, Junghyo Jo, Masaki Nagaya, J. Michael Millis, Hiroshi Nagashima, Hitomi Matsunari and Piotr Witkowski. Their work appears in journals such as Journal of Clinical Oncology, Physical review. E, Journal of Physics Condensed Matter, PLoS ONE and Cancer Research.
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.