Dexiang Yang
Impact in
- Infectious Diseases top 5%
- COVID-19 Clinical Research Studies
- SARS-CoV-2 and COVID-19 Research
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- Antibiotic Use and Resistance
Papers in
-
- COVID-19 Clinical Research Studies 4
- SARS-CoV-2 and COVID-19 Research 2
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- COVID-19 diagnosis using AI 2
- Co-authors
- Min Zhou (4 shared papers)Jian Li (3 shared papers)Yun Ling (3 shared papers)Fangying Lu (3 shared papers)Jieming Qu (2 shared papers)Yuqing Chen (1 shared paper)Jie Huang (2 shared papers)Rong Chen (2 shared papers)
- Journals
- Epidemiology and Infection (1 paper)Saudi Journal of Biological Sciences (1 paper)Journal of Diabetes (1 paper)Annals of Translational Medicine (1 paper)American Journal of Respiratory and Critical Care Medicine (1 paper)
- Partner nations
- ChinaUnited StatesKuwait
In The Last Decade
Dexiang Yang
7 papers receiving 654 citations
Dexiang Yang's Hit Papers
Peers
Comparison fields: 5 of 77
- Infectious Diseases 409
- Applied Microbiology and Biotechnology 26
- Neurology 181
- Critical Care and Intensive Care Medicine 27
- Health Informatics 5
Countries citing papers authored by Dexiang Yang
This map shows the geographic impact of Dexiang Yang'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 Dexiang Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dexiang Yang more than expected).
Fields of papers citing papers by Dexiang Yang
This network shows the impact of papers produced by Dexiang Yang. 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 Dexiang Yang. The network helps show where Dexiang Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Dexiang Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | COVID-19 with Different Severities: A Multicenter Study of Clinical Features Hit paper breakdown → | 2020 | 602 |
| 2 | 2020 | 27 | |
| 3 | 2021 | 20 | |
| 4 | 2020 | 8 | |
| 5 | 2017 | 6 | |
| 6 | 2023 | 2 | |
| 7 | Clinical characteristics of 14 cases of Chlamydia psittaci pneumonia. | 2024 | 1 |
| 8 | A Deep Learning Pipeline for Accurate Differential Diagnosis between Novel Coronavirus Pneumonia and Influenza Pneumonia | 2020 | 0 |
About Dexiang Yang
Dexiang Yang is a scholar working on Infectious Diseases, Radiology, Nuclear Medicine and Imaging, Surgery, Pathology and Forensic Medicine and Neurology, having authored 8 papers that have together received 666 indexed citations. Recurring topics across this work include COVID-19 Clinical Research Studies (4 papers), COVID-19 diagnosis using AI (2 papers), SARS-CoV-2 and COVID-19 Research (2 papers), Cancer Mechanisms and Therapy (1 paper), Long-Term Effects of COVID-19 (1 paper), Lung Cancer Treatments and Mutations (1 paper), Reproductive tract infections research (1 paper) and Urinary Tract Infections Management (1 paper). The work is most often cited by research in Infectious Diseases (409 citations), Applied Microbiology and Biotechnology (26 citations), Neurology (181 citations), Critical Care and Intensive Care Medicine (27 citations) and Health Informatics (5 citations). Dexiang Yang has collaborated with scholars based in China, United States and Kuwait. Frequent co-authors include Min Zhou, Jian Li, Yun Ling, Fangying Lu, Jieming Qu, Yuqing Chen, Jie Huang, Rong Chen, Tao Bai and Xin Li. Their work appears in journals such as Epidemiology and Infection, Saudi Journal of Biological Sciences, Journal of Diabetes, Annals of Translational Medicine and American Journal of Respiratory and Critical Care Medicine.
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.