Hai‐Yan Nan
- Genetics top 5%
- Glioma Diagnosis and Treatment 6
- Neurology top 5%
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- Radiomics and Machine Learning in Medical Imaging 7
- MRI in cancer diagnosis 4
- Health Informatics top 10%
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- Pulmonary Hypertension Research and Treatments 3
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis 2
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- Migraine and Headache Studies 2
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- Hepatocellular Carcinoma Treatment and Prognosis 2
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- Cancer, Hypoxia, and Metabolism 2
- Co-authors
- Lin‐Feng YanGuangbin CuiWen WangYu‐Chuan HuBo HuYu HanYing YuYang Yang
- Journals
- Journal of Magnetic Resonance Imaging (2 papers)Experimental and Toxicologic Pathology (2 papers)Channels (1 paper)
- Partner nations
- ChinaUnited States
In The Last Decade
Hai‐Yan Nan
26 papers receiving 825 citations
Peers
Comparison fields: 5 of 88
- Genetics 290
- Neurology 223
- Radiology, Nuclear Medicine and Imaging 468
- Health Informatics 15
- Computer Vision and Pattern Recognition 120
Countries citing papers authored by Hai‐Yan Nan
This map shows the geographic impact of Hai‐Yan Nan'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 Hai‐Yan Nan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hai‐Yan Nan more than expected).
Fields of papers citing papers by Hai‐Yan Nan
This network shows the impact of papers produced by Hai‐Yan Nan. 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 Hai‐Yan Nan. The network helps show where Hai‐Yan Nan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Hai‐Yan Nan, 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 | 2023 | 1 | |
| 2 | 2023 | 1 | |
| 3 | 2022 | 2 | |
| 4 | 2022 | 17 | |
| 5 | 2021 | 4 | |
| 6 | 2021 | 34 | |
| 7 | 2019 | 67 | |
| 8 | 2019 | 10 | |
| 9 | 2019 | 37 | |
| 10 | 2018 | 3 | |
| 11 | 2018 | 210 | |
| 12 | 2018 | 24 | |
| 13 | 2017 | 36 | |
| 14 | 2017 | 3 | |
| 15 | 2016 | 3 | |
| 16 | 2013 | 32 | |
| 17 | 2013 | 9 | |
| 18 | 2010 | 15 | |
| 19 | 2010 | 22 | |
| 20 | Reputation Computing for Wireless Sensor Networks Based on Dirichlet Distribution | 2009 | 4 |
About Hai‐Yan Nan
Hai‐Yan Nan is a scholar working on Genetics, Neurology and Radiology, Nuclear Medicine and Imaging, having authored 26 papers that have together received 837 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (7 papers), Glioma Diagnosis and Treatment (6 papers), MRI in cancer diagnosis (4 papers), Pulmonary Hypertension Research and Treatments (3 papers), Migraine and Headache Studies (2 papers), Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis (2 papers), Hepatocellular Carcinoma Treatment and Prognosis (2 papers) and Cancer, Hypoxia, and Metabolism (2 papers). The work is most often cited by research in Genetics (290 citations), Neurology (223 citations) and Radiology, Nuclear Medicine and Imaging (468 citations). Hai‐Yan Nan has collaborated with scholars based in China and United States. Frequent co-authors include Lin‐Feng Yan, Guangbin Cui, Wen Wang, Yu‐Chuan Hu, Bo Hu, Yu Han, Ying Yu, Yang Yang, Ying‐Zhi Sun and Qiang Tian. Their work appears in journals such as Journal of Magnetic Resonance Imaging, Experimental and Toxicologic Pathology, Channels, Neuroscience and Journal of Surgical 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.